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Speaker 1: tech Stuff from House Touffi dot com. Hello again, everyone,
Speaker 1: and welcome to tech Stuff. My name is Chris Poulette,
Speaker 1: and I am an editor at how Stuff works dot com.
Speaker 1: Sitting across from me in a cape and tights is
Speaker 1: senior writer Jonathan Strickland. Hey there, citizen. So, so, Chris,
Speaker 1: if I were to ask you, just off the top
Speaker 1: of your head, how would you define a supercomputer? What
Speaker 1: would you say? Well, if I hadn't already made the joke,
Speaker 1: I would have said it was a computer in the
Speaker 1: Cape and tights, But no, I'm honestly I would say
Speaker 1: supercomputer is a computer that can do a lot more
Speaker 1: calculations in a shorter period of time than the machines
Speaker 1: sitting on our desktop. Yeah. I think of it as
Speaker 1: sort of the bleeding edge of what a computer is
Speaker 1: capable of doing. Something that that still feels fills a room,
Speaker 1: even though typical computers these days don't need to fill
Speaker 1: a room because it's that big, it still has that
Speaker 1: much computing power, right right, And the term comes from
Speaker 1: the nineteen sixties and uh. In order to really kind
Speaker 1: of understand the the the span of this, I think
Speaker 1: I was going to talk a little bit about the
Speaker 1: last computer I could find that was a powerful computer
Speaker 1: that existed before people started talking about super computers, which
Speaker 1: was the IBM seventy thirty stretch. Yes, that was the
Speaker 1: one that was made with elastic. Yes, ye gain a
Speaker 1: couple of pounds. Your computer can still you know fit.
Speaker 1: It was Mr. Fantastic of the computer world. No, because
Speaker 1: it was not a super computer. It took up two
Speaker 1: thousand square feet back in the day, this being the
Speaker 1: early sixties, Higger than my two thousand square feet. It
Speaker 1: cost thirteen million dollars, which if you were to translate
Speaker 1: to today's cash, would be ninety one million dollars. It's
Speaker 1: a lot of cash. So that was the fastest computer
Speaker 1: at the time until a fellow named Seymour Roger Craig
Speaker 1: showed up. I s Mr Craze. Yeah. Craig ends up
Speaker 1: being a big name in supercomputers, particularly in the sixties,
Speaker 1: seventies and up to the mid eighties. That was the
Speaker 1: name in supercomputers. And he was working for a company
Speaker 1: called Engineering Research Associates or E r A, which actually
Speaker 1: grew out of a naval operation, um being the U. S. Navy,
Speaker 1: not belly Button. I was gonna you were looking at
Speaker 1: me like joke. No, No, not that it was a
Speaker 1: Navy project. How about that as in as in the
Speaker 1: military force, not the color It was a Navy project
Speaker 1: that was all about code breaking, all right. So there
Speaker 1: was this project about code breaking that eventually kind of
Speaker 1: spun off and became an actual company all on its
Speaker 1: own called Engineering Research Associates, and it branched out beyond
Speaker 1: code breaking, although it took all the code breaking work
Speaker 1: it could get. Yeah, we talked about the Enigma UM
Speaker 1: some episodes back UM and we were talking about the
Speaker 1: bomb UM and yeah, those early uh that was really
Speaker 1: the early application for supercomputers was you know, needing to
Speaker 1: crunch a lot of data very quickly, and there weren't
Speaker 1: There weren't the kind of applications that we have now.
Speaker 1: We'll get into that, I'm sure in a few minutes,
Speaker 1: but but yeah, I mean that was you know, why
Speaker 1: would you need a supercomputer? You know? That was That's
Speaker 1: probably about the only thing I could think of where
Speaker 1: people were needing to crunch that kind of information as
Speaker 1: quickly as possible and defense. Typically, especially with the early supercomputers,
Speaker 1: they were really designed for very specialized suting, so not
Speaker 1: necessarily specialized from the ground up for a one particular
Speaker 1: type of computing, but they were. They were not meant
Speaker 1: to be general computers. They were meant to do tip
Speaker 1: no admiral computers, that's true. Uh No, they were. They
Speaker 1: were meant to do a specific task very very well.
Speaker 1: And that's that's all they were meant to do. Um. Now,
Speaker 1: Craig had an interesting philosophy, he said, and this is
Speaker 1: this is a quote from him. He said, anyone can
Speaker 1: build a fast CPU. The trick is to build a
Speaker 1: fast system. And that was the secret to Craig creating
Speaker 1: the first supercomputer. He realized that if you created a
Speaker 1: processor that was really really fast, that did not matter
Speaker 1: if it couldn't get the data it needed to execute
Speaker 1: operations upon fast enough. So he saw the need to
Speaker 1: create a system that would move data through very very quickly,
Speaker 1: not just processed data, but move it so that means
Speaker 1: needs a lot of memory, It needs a very fast
Speaker 1: pathway from memory to processor. There are a lot of
Speaker 1: pieces that have to be put in place, and he
Speaker 1: saw this very early on, and so using that philosophy,
Speaker 1: he designed a computer back in nineteen sixty two that
Speaker 1: was called the c d C sixty six hundred. Now
Speaker 1: CDC stands for Controlled Data Corporation. Yeah, um, uh e
Speaker 1: R A was taken over by Remington Rand UM and
Speaker 1: that's uh, that's the name I remember because you know,
Speaker 1: UH still remember a lot of those old machine names
Speaker 1: UM from stuff that I found in my uh dad's collection.
Speaker 1: Of course, he was, you know, a mechanical engineer UM
Speaker 1: before he retired, and you know, so he was interested
Speaker 1: in all kinds of machines and I didn't know what
Speaker 1: I was looking at at the time, of course, you know,
Speaker 1: but they were all these UM science and computing magazines
Speaker 1: laying around and that name I recognized also UNITIS because
Speaker 1: Remington Ran became Unities UM, and probably a lot more
Speaker 1: of our listeners are familiar with that name. But he
Speaker 1: partnered with William Norris to start Controlled out of Corporation
Speaker 1: UM back in ninety seven. UM. And really at that point,
Speaker 1: the UNIVAC from Remington Rand and IBM were the computing companies. Yeah,
Speaker 1: you know, IBM has been the heavyweight for so long,
Speaker 1: but CDC was the first uh you know, upstart to
Speaker 1: really make a dent in there, uh stranglehold on the industry.
Speaker 1: And and Craig wanted to join CDC fairly early on,
Speaker 1: but apparently he was needed for a project UM that
Speaker 1: would not let him leave exactly what he wanted to.
Speaker 1: So once he did leave, that's when he designed the
Speaker 1: CDC sixty DRED, which was officially announced in nineteen sixty four,
Speaker 1: so designed in sixty two, announced two years later, and
Speaker 1: it was the first commercially successful supercomputer, with a price
Speaker 1: tag of between seven and eight million dollars, sometimes going
Speaker 1: up as high as ten million, depending upon the configuration
Speaker 1: that the customer wanted. UM Now, in today's cash, that
Speaker 1: would equal about sixty million dollars, so thirty one million
Speaker 1: dollars cheaper in today's money than the Stretch computer, and
Speaker 1: it was actually much more powerful. It had four hundred
Speaker 1: thousand transistors and one hundred miles of wiring, and it
Speaker 1: was the size of about four filing cabinets, so it
Speaker 1: was also significantly smaller than the Stretch, didn't take up
Speaker 1: two thousand square feet. The clock speed was around a
Speaker 1: hundred nanoseconds, and it had sixty five thousand sixty bit
Speaker 1: words of memory. So this is kind of an odd
Speaker 1: time in computing. We hadn't really settled on the thirty
Speaker 1: two sixty four bit kind of model. This was before that. UM.
Speaker 1: It also used six high speed drums as sort of
Speaker 1: a temporary storage area. It had a central storage that
Speaker 1: used magnetic tape, and it used the four trans sixty
Speaker 1: six compiler. UM the equivalent to today's machines means that
Speaker 1: it would have about a ten mega hurts processor. Yeah,
Speaker 1: well that could work up to forty mega hurts and speed. Well,
Speaker 1: it could do a three million floating point operations per second. Yeah,
Speaker 1: so three million. That would be a mega flop. Three
Speaker 1: mega flops, right, So we're gonna get into lots of
Speaker 1: different flop terms later as well, and they get incredibly huge.
Speaker 1: H Of course, you have to keep it cool because
Speaker 1: otherwise it breaks out into a flop sweat. That's true. Uh, well,
Speaker 1: not the flop sweat part, but you do have to
Speaker 1: keep it cool. As we know electronics, when you're running
Speaker 1: electricity through them, one of the byproducts is heat, and heat,
Speaker 1: as it turns out, is not a great thing for
Speaker 1: electronic components. It can make stuff expand contacts can lose connections,
Speaker 1: so that stuff starts to malfunction and entire system could
Speaker 1: shut down. So the CDC STRED had a cooling system
Speaker 1: that was provided by a special chemical free on. Really, yeah,
Speaker 1: they used free on to cool the system. In fact,
Speaker 1: it was they would use free on for a while
Speaker 1: before finally having to switch to a different coolant because
Speaker 1: free on just wasn't efficient enough. Eventually, now at the
Speaker 1: hundred it was still doing the job. So Kari was
Speaker 1: also an innovator in another way. UM, the stretch IBM
Speaker 1: stretched the UM was sort of a hybrid machine. They
Speaker 1: had transistors and vacuum tubes in it UM, and that's
Speaker 1: I think why one of the reasons why craze machines
Speaker 1: were smaller. The sixteen O four, which proceeded the sixty
Speaker 1: six hundred UM, was the one of the very first
Speaker 1: to use transistors only. So there was also a transistor
Speaker 1: machine and so it would take up a lot less
Speaker 1: space than the vacuum tubes. And I would imagine that
Speaker 1: based on my knowledge, my personal knowledge of vacuum tubes,
Speaker 1: might have been a little cooler simply because of that. Yeah,
Speaker 1: I would imagine that they wouldn't have had to have
Speaker 1: as dramatic and a c system to keep the the
Speaker 1: room bearable because vacuum tubes do put off a lot
Speaker 1: of heat. Um Another interesting IBM CDC connection here is
Speaker 1: that Thomas Watson Jr. Which was IBM s C. He
Speaker 1: was IBM CEO at the time, wrote a famous memo
Speaker 1: that time two IBM employees, and he said, last week
Speaker 1: Controlled Data announced the sixty system. I understand that in
Speaker 1: the laboratory developing the system, there are only thirty four people,
Speaker 1: including the janitor. Of these fourteen our engineers and for
Speaker 1: our programmers. Contrasting this modest effort with our vast developmental activities,
Speaker 1: I failed to understand why we have lost our industry
Speaker 1: leadership position by letting someone else offer the world's most
Speaker 1: powerful computer. Craize's response was a reportedly, well, there's your problem. Essentially,
Speaker 1: Craig was saying that, you know, perhaps IBMS approach it
Speaker 1: was a little burdened by size that IBM had grown
Speaker 1: so large that to manage a project like this was
Speaker 1: very difficult to do because it was just too big.
Speaker 1: So that's an interesting idea that an organization needed to
Speaker 1: be kind of small and nimble in order to pull
Speaker 1: something off like creating the world's fastest computer. He followed
Speaker 1: up the c d C sundred with which had a
Speaker 1: sixty five thousand, five hundred thirty six sixty bit word
Speaker 1: memory and a clock speed of twenty seven nano seconds
Speaker 1: uh and actually in practice ran about five times faster
Speaker 1: than the six. But then Cray left c d C
Speaker 1: and he formed his own company, Cray Research two and
Speaker 1: in nine six he introduced the Kray one, which if
Speaker 1: you've ever heard the craze supercomputer, that's what this is.
Speaker 1: It's the crazy one was the first of those. It
Speaker 1: had a clock speed of a well, it's processor ran
Speaker 1: at eighty mega hurts and back at this time these
Speaker 1: supercomputers were still using a single CPU, so that was
Speaker 1: kind of interesting to these were single CPU systems. So
Speaker 1: it had eighty mega hurts processor, sixty four bit system,
Speaker 1: it ran at a hundred thirty six mega flops, so
Speaker 1: one or thirty six million floating operations per second, and
Speaker 1: it had one thousand, six hundred sixty two printed circuit
Speaker 1: boards that made up the components of this computer. It
Speaker 1: costs between five and eight million dollars, depending on how
Speaker 1: you wanted it set up, and in today's cash, that's
Speaker 1: about twenty five million dollars. So we see that the
Speaker 1: processor speed is increasing and the price is coming down.
Speaker 1: Often the size of the computer is decreasing as well,
Speaker 1: although that that also flip flops over the years because
Speaker 1: while the solid state electronics definitely brought the size down,
Speaker 1: eventually the way we pack in more speed requires more space.
Speaker 1: But we'll get into that. Okay, So after the Cray
Speaker 1: one came the Cray x MP in Yeah, this is uh,
Speaker 1: this is interesting because Crai realized also in addition to
Speaker 1: the fact that he knew that the components, the all
Speaker 1: of the components, the entire machine was important and not
Speaker 1: just a processor, he also realized that, uh early on,
Speaker 1: that parallel processing could also speed things up. Um. Now
Speaker 1: it's common for us to have multi core processes in
Speaker 1: our desktop machines or laptops, or in fact, now we're
Speaker 1: starting to see them in our mobile devices. Um. But
Speaker 1: you know, at the time in the seventies and eighties,
Speaker 1: this was still something sort of newish, um, and it's
Speaker 1: not something that everybody realized. Uh. So the x MP
Speaker 1: actually was to Cray one computers linked together um and
Speaker 1: using those two machines together in a multiprocessing effort UM
Speaker 1: they could triple the performance of just one Cray one UM,
Speaker 1: which is something interesting to note. And uh, the Create
Speaker 1: two had four processors in the same machine, and that
Speaker 1: was the first to exceed one billion flops as Britainic
Speaker 1: it tells me. Yeah, uh, it actually could have up
Speaker 1: to eight CPUs c um. The these machines often over
Speaker 1: time were upgraded, so the initial step specs you would
Speaker 1: get when they were first released were one thing, and
Speaker 1: then by the end of the run of production they
Speaker 1: would be better. I mean, which makes sense. I mean
Speaker 1: we see that in computers all the time. We definitely
Speaker 1: we tend to call them different model numbers now, but
Speaker 1: the same sort of thing happens. So back in two
Speaker 1: you had this Cray XMP with a hundred five mega
Speaker 1: hurts CPUs running around two hundred megaflops each. Uh, and
Speaker 1: if they had up to four CPUs you could get
Speaker 1: eight hundred megaflops going and that was pretty impressive and
Speaker 1: had the equivalent, by the way of a hundred and
Speaker 1: twenty eight megabytes of RAM. So yeah, you think about
Speaker 1: that one or twenty eight megabytes of RAM in that
Speaker 1: was considered bleeding edge for a supercomputer. UM and the
Speaker 1: storage units for the Cray XMP were the size of
Speaker 1: a file cabinet and they could hold up to twelve
Speaker 1: gigs of storage because they have a flash in my
Speaker 1: bag with me eight gigs. Yeah, and you can find,
Speaker 1: you can find, you can find twenty gig or more
Speaker 1: flash drives, which you know, you think about that, that's
Speaker 1: something that is small enough for you to carry on
Speaker 1: a key chain. While back in nineteen eighty two you
Speaker 1: had a file cabinet sized device that could hold twelve
Speaker 1: gigs and that was considered massive, like a massive amount
Speaker 1: of information. So, yeah, time really does change things, doesn't it.
Speaker 1: So yeah, the Cry two, that's when they switched from
Speaker 1: free On to Flora Nert as their coolant. I'm sorry,
Speaker 1: but that sounds like a made up alien name from
Speaker 1: a from a an animated movie. Technically all names are
Speaker 1: made up. I know, that's I just blew your mind.
Speaker 1: What if there were no hypothetical questions? Turn on the yes.
Speaker 1: So the Floria Nert the reason why they switched was
Speaker 1: because they had at that point packed the components so
Speaker 1: tightly together that free on was not efficient enough to
Speaker 1: cool them, so they switched from free on to Flora
Speaker 1: Nert and it's a little Floria Nert I've had around somewhere.
Speaker 1: Then they also had to figure a new way to
Speaker 1: access the memory on the Create too, because at this
Speaker 1: point they had reached that that point that Krey had
Speaker 1: mentioned earlier about creating a CPU that can process information
Speaker 1: faster than it can pull information in. So they found
Speaker 1: they would actually dedicate processors to getting data from memory
Speaker 1: and funneling it into the central processing units. And UH,
Speaker 1: this was this was really important. It was what kind
Speaker 1: of led the way into threading and and loading memory
Speaker 1: CPUs that have that capability to load information from memory
Speaker 1: preloading things that kind of came out of this work.
Speaker 1: In fact, a lot of the UH, the advances that
Speaker 1: we see in personal computers UM are really possible because
Speaker 1: of the pioneering work that was done in supercomputers. It
Speaker 1: was stuff that that found its way from the engineering
Speaker 1: of supercomputers into personal computers, often a completely different sense
Speaker 1: of scale, but a similar approach. Now after the Create too,
Speaker 1: that's when Japan started to produce some supercomputers that were
Speaker 1: UH that were actually faster than anything that was being
Speaker 1: produced in the United States. So up until this point,
Speaker 1: it was all the US that was they dominated that,
Speaker 1: that country dominated the supercomputer industry. But in n six,
Speaker 1: so this is you know again, the Kray craze, if
Speaker 1: you will, lasted from the sixties all the way into
Speaker 1: the eighties. Well ninety six, Japan introduced the s R
Speaker 1: twenty two oh one, which had two thousand forty eight processors.
Speaker 1: So remember create two. That was up to eight processors.
Speaker 1: The s R two two oh one two thousand forty
Speaker 1: eight processors. I count two thousand forty more processors with
Speaker 1: that computer. Then with the Cray, do my math could
Speaker 1: be off in an English major and it could it
Speaker 1: could have up to six hi flops of processing. That's
Speaker 1: kind of crazy. Um yeah. I also I also feel
Speaker 1: like we would be remissed to mention the efforts of
Speaker 1: Danny Hillis um W. Daniel Hillis was a grad student
Speaker 1: at m i T. Massachusetts Institute of Technology when he
Speaker 1: realized that distributing computing was the way of the future,
Speaker 1: if you will. UM he started thinking Machines Corporation in
Speaker 1: UM and this CM one, which was the first of
Speaker 1: his machines to come out in eight five. Um it
Speaker 1: had sty six one bit processors grouped sixteen to a chip. Interesting,
Speaker 1: that's a that's a really interesting approach tiny tiny processors. Yeah,
Speaker 1: so you know, but yeah, I didn't come across that
Speaker 1: my um my, my research, which is why this is
Speaker 1: actually really like I'm my mind is really as I'm
Speaker 1: thinking about that sort of archetype. Sure, that's really an
Speaker 1: interesting approach. Well, it's interesting too to see how different
Speaker 1: See Jonathan and I do our research separately on purpose
Speaker 1: so that we uh come up with different things on
Speaker 1: the cases. And um, so it's funny that that I
Speaker 1: would have come across that. Also. Well, I think of
Speaker 1: Danny hillis because I've seen his name a lot in
Speaker 1: things like along Now Foundation and people with he he
Speaker 1: hangs out with people like Stewart Brandon, Kevin Kelly, UM,
Speaker 1: fascinating people. But um anyway, yeah, that that's uh, that
Speaker 1: was one of his contributions. And you see that in
Speaker 1: again in today's machines. I mean, we have this, you know,
Speaker 1: with us every day. But you know this is uh,
Speaker 1: this is when we started to realize that you don't
Speaker 1: necessarily have to go buy More's Law and wait until
Speaker 1: next year's chip comes out with twice as many processors
Speaker 1: on it. You can you can do this by dividing
Speaker 1: up the work. Yeah, and and in fact, that's another
Speaker 1: good point about the SR two oh one, the computer
Speaker 1: from Japan, because, uh, in order to who use these
Speaker 1: two thousand forty eight processors, there was a new development
Speaker 1: in computer science which was called multiple instruction multiple data
Speaker 1: or m I m D. Now, this is the idea
Speaker 1: of being able to solve problems by pulling in information
Speaker 1: from from memory and feeding it to different processors that
Speaker 1: are all using different operations on that data to come
Speaker 1: to a single solution, not necessarily a single solution, but
Speaker 1: that's I'm using that as as an example for this
Speaker 1: for this explanation. So this m I m D approach
Speaker 1: is what allowed us to develop multi core processors, because
Speaker 1: in this case we're still talking about single processors that
Speaker 1: are all grouped together. Eventually we will get to the
Speaker 1: point where we have multi core processors where a single
Speaker 1: processor has multiple cores and each core can work on
Speaker 1: part of a problem or separate problems to solve things faster,
Speaker 1: to to get to a inclusion faster than they would
Speaker 1: if it was just one single processor, even if it
Speaker 1: was a really really fast processor working on a series
Speaker 1: of problems. So I always I always use this analogy.
Speaker 1: Imagine that you have one super smart math genius taking
Speaker 1: a math test, and the math genius is going through
Speaker 1: and solving all of these problems, and he or she
Speaker 1: is able to do this flawlessly, able to solve all
Speaker 1: the problems, but it takes a certain amount of time
Speaker 1: to get through the test. Then you get that same
Speaker 1: test to four above average math students. They're not geniuses,
Speaker 1: but they're there. They can hold their own. And you
Speaker 1: divide it up, say all right, you take this this
Speaker 1: one fourth of the test, you take this quarter, you
Speaker 1: take this quarter, and you take that quarter, and the
Speaker 1: four students together start to work. Those four students are
Speaker 1: very likely going to be able to finish the entirety
Speaker 1: of that test much faster, each of them working on
Speaker 1: a quarter of it, rather than the genius who is
Speaker 1: working on the full thing at the same time. Even
Speaker 1: though the genius is smarter and can work faster on
Speaker 1: each individual problem, collectively those four students are going to
Speaker 1: solve that test faster. That's the philosophy behind both grouping
Speaker 1: cores together and making them a parallel processing unit or
Speaker 1: taking a multi core approach to a CPU. Yep, and
Speaker 1: you can. You can thank Danny Hillis for figuring out
Speaker 1: the idea of massively parallel computing UM. But you know
Speaker 1: that that's a problem though too, because instead of having
Speaker 1: two machines running side by side and linked together, now
Speaker 1: you have to figure out how you're going to parse
Speaker 1: all those instructions between all those different processors. So you
Speaker 1: have to have the software or the operating system that
Speaker 1: will give instructions to each of the processors actively and
Speaker 1: direct essentially directing traffic. Yes, this is this is kind
Speaker 1: of like, it's not. It's not a simple thing to
Speaker 1: figure out. It reminds me of Intel's to talk approach
Speaker 1: to developing processors. You think of the TICK being the
Speaker 1: physical machinery that's going to do the processing, and you
Speaker 1: think of the talk as the software that's optimized to
Speaker 1: work on that physical hardware to make it really live
Speaker 1: up to its potential. And then you have another tick
Speaker 1: where you've got an advancement in the physical hardware, but
Speaker 1: perhaps the last generation of software isn't really optimized to
Speaker 1: work on that, so you have to make new software.
Speaker 1: This is a continuation. In fact, that's one of the
Speaker 1: things that people say is a barrier to artificial intelligence
Speaker 1: to the point of having a a computer that has
Speaker 1: self awareness. It's not necessarily that we can't reach the
Speaker 1: physical uh requirements we would need in order to have
Speaker 1: a computer be able to have some form of self awareness.
Speaker 1: It's the idea that we could throw as much horsepower
Speaker 1: at the problem as we wanted to. Without the software,
Speaker 1: it just won't happen anyway. Get back to supercomputers specifically,
Speaker 1: there's one company name we haven't really mentioned yet, and
Speaker 1: it's big. I mean, we talked about a little bit
Speaker 1: just then, but not in the terms of supercomputers. It's
Speaker 1: a big name in computer architecture, but it wasn't a
Speaker 1: really big name in the whole supercomputer story. And that's Intel. Now,
Speaker 1: Intel was not just sitting back during this whole time. Now, granted,
Speaker 1: Intel's main focus is on enterprise and consumer processors, which
Speaker 1: are not completely analogous to what is you you find
Speaker 1: in supercomputers at this time. Right, that would change, but
Speaker 1: not immediately. But Intel did develop something called the Paragon,
Speaker 1: which was supposed to be, you know, another fantastic supercomputer,
Speaker 1: and it could support up to four thousand processors using
Speaker 1: this m I M D architecture. But it did not
Speaker 1: succeed in the market. It just sort of well, it
Speaker 1: lopped in a different way, the other kind of flawed,
Speaker 1: the bad kind, so that didn't really go anywhere, but
Speaker 1: it did again sort of push this trend of parallel
Speaker 1: processing and m I M D. Uh. The Japanese came
Speaker 1: out with a couple of other computers called as Key
Speaker 1: Read and Asky White Until also had an Asky Read. Um. Yeah.
Speaker 1: Well actually this this goes back to the Comprehensive Test
Speaker 1: Band Treaty. Uh. The United States signed UM they needed
Speaker 1: a certification program for the nuclear weapons that they had
Speaker 1: built up and uh so what they started was the
Speaker 1: Accelerated Strategic Computing Initiative. ASKI with only one eye instead
Speaker 1: of asking characters with two eyes, just to clarify, I'm
Speaker 1: glad you did, thank you, uh, and ask you Read
Speaker 1: Yes was built at Sandy and National Laboratories and No Albuquerque,
Speaker 1: New Mexico, UNTIL helped them out with that, and then
Speaker 1: that was the first machine to get a Tarra flop. Yeah,
Speaker 1: and it was the first one to break the Tarra
Speaker 1: flop barrier. It did that with six thousand, two hundred
Speaker 1: mega hurts pentium pro processors, seventy two of them, well
Speaker 1: six thousand at first. It then eventually was upgraded. The
Speaker 1: very first one had six thousand and the very last
Speaker 1: one had nine thousand M two Xeon processors, and it
Speaker 1: actually hit three point one tarra flops at the end
Speaker 1: of its production life. So yeah, like I said, you know,
Speaker 1: when we give these numbers, there are different ones because
Speaker 1: there's a certain amount that was available when the computer
Speaker 1: first premiered, then there was like the average amount during
Speaker 1: the computer's lifetime, and then the amount that was available
Speaker 1: at the very end of its run time. So these
Speaker 1: numbers do change a little bit depending upon which source
Speaker 1: you're reading in which version of the computer they're looking at,
Speaker 1: because again, these computers are they come in a range
Speaker 1: of models, so not all of them are exactly the same. Now,
Speaker 1: while we talk about uh playing games like chess, you
Speaker 1: know that that's one of the big uh consumer UH
Speaker 1: visibility issues with supercomputer You don't see what supercomputers do.
Speaker 1: And that was a way for them, the IBM, and
Speaker 1: in particular to achieve notice, was taking on people like
Speaker 1: Gary Kasparov chess masters worldwide with a supercomputer kind of
Speaker 1: computer outthink quote unquote out think a human. Well, the
Speaker 1: point of ASKI was again one of those behind the
Speaker 1: scenes thing. It was a very military thing. It was
Speaker 1: more like Whopper in more games. Actually, uh actually exactly
Speaker 1: like that. The point was to simulate nuclear tests. Um.
Speaker 1: And that was why they needed a lot of computing power,
Speaker 1: uh and something a machine that could run a lot
Speaker 1: of calculations very quickly, because they wanted to, uh, you know,
Speaker 1: this is not something you want to do. Hey, well
Speaker 1: let's uh, let's test out fifty nuclear warheads. Yeah this,
Speaker 1: you know. They wanted to do this with a computer
Speaker 1: simulation and uh so that's why they started the initiative.
Speaker 1: It was not a game, but a challenge. Hey let's
Speaker 1: you know, let's keep coming up with newer faster machines
Speaker 1: because we need newer faster machines to run nuclear simulations. Yeah.
Speaker 1: And simulations in general were a big part of what
Speaker 1: these supercomputers were put to use for. I mean like
Speaker 1: climatology for example, weather predictions. That was a big requirement
Speaker 1: as well as supercomputers have been put towards that to
Speaker 1: try and help map and predict climate change and just
Speaker 1: weather patterns, not not just climate but weather day to
Speaker 1: day weather, and also other simulations as well. Not to
Speaker 1: mention crunching data from facilities that generate lots and lots
Speaker 1: of information. So um, things like the CET Institute would
Speaker 1: for extraterrestrial intelligence. Yes that they would use very powerful
Speaker 1: computers to try and crunch all the data they would
Speaker 1: get from radio telescopes. You also had things like the
Speaker 1: Large Hadron Collider and other supercolliders that generate lots and
Speaker 1: lots of data and they need these really fast computers
Speaker 1: in order to process the data and make it meaningful.
Speaker 1: So um. Moving on. So right around this time when
Speaker 1: the asky read comes out, Um, that's when there was
Speaker 1: a shift in supercomputing. So before there were all these
Speaker 1: customized uh computers that had their own processors or had
Speaker 1: thousands of processors running together. Uh. But at this point
Speaker 1: it became possible to actually build a supercomputer with off
Speaker 1: the shelf parts. You could actually get enough computers together
Speaker 1: and linked them together to perform as a supercomputer. And
Speaker 1: this was also when there became a shift to using
Speaker 1: the Linux operating system. UH. So Lenox kind of replaces
Speaker 1: Unix as the OS of choice for people who are
Speaker 1: designing supercomputers, which is nice because now you can tell
Speaker 1: the company nurse never mind. In two thousand two, Japan
Speaker 1: comes back with the as Key White, where it's had
Speaker 1: a thirty five terra flops computer. It was the NBC
Speaker 1: Earth Simulator, and it cost a hair under a billion
Speaker 1: dollars million. It's a lot of hairs, actually millions, a
Speaker 1: lot of hairs. If anyone wants to give me a
Speaker 1: hair in that sense, I will take it. Uh. And
Speaker 1: two thousand four, IBM comes out with the Blue Gene
Speaker 1: slash L computer and had sixteen thousand computer nodes and
Speaker 1: each node had two CPUs. I'm gonna be thinking Bowie
Speaker 1: the rest of the day now, So yeah, thirty two
Speaker 1: thousand CPUs. Ultimately, if my math is correct, and then
Speaker 1: that could run it's seventy terra flops, so twice as
Speaker 1: fast as the Askey White, and a two thousand seven
Speaker 1: version of this could actually manage up to six hundred
Speaker 1: tarra flops and it had a hundred thousand computer nodes,
Speaker 1: so two hundred thousand processors. With that starting to get
Speaker 1: into some preretty ridiculous computers from you know if you're
Speaker 1: looking at it as, Hey, I own a computer that's
Speaker 1: got a single processor. This one has a two hundred
Speaker 1: thousand of them. Yeah. Yeah. It also sort of UHUM
Speaker 1: makes apples claim. In the late nineties, UM sort of silly, UM,
Speaker 1: because well, the federal government classified a supercomputer UM. I
Speaker 1: can't remember exactly when it was, it was in the
Speaker 1: late nineties, and UH as as a machine that would
Speaker 1: run a giga flop and UM IBM when they were
Speaker 1: still running on power process power PC processors. UM, there
Speaker 1: was a Mac that they advertised as being a supercomputer
Speaker 1: because it could reach a giga flop. UM. And I
Speaker 1: just thought at the time it was kind of weird
Speaker 1: to think about, UM, But now it's just kind of
Speaker 1: silly when you take it into context and these these
Speaker 1: actual supercomputers at the time. Uh, A gigga flop is good,
Speaker 1: but no, right, So a mega flop is a million
Speaker 1: floating operations per second, A gigga flop is a billion
Speaker 1: floating operations per second, A tarra flop is a trillion
Speaker 1: floating operations per second. Well, and which is a quadrillion
Speaker 1: floating operations per second per second. Yeah, quadrillion and the
Speaker 1: first supercomputer to hit that and break that barrier was
Speaker 1: another IBM machine, the road Runner, and uh it had
Speaker 1: twenty thousand CPUs and it was the first computer to
Speaker 1: break that pedal flop barrier. So one quadrillion floating operations
Speaker 1: per second, it's a serious machine. In twous ten, there
Speaker 1: was an interesting development because China entered the supercomputer FRAY.
Speaker 1: Now at this point it was really a battle down
Speaker 1: between the United States and Japan, and Germany also has
Speaker 1: quite a few supercomputers as well, but but US and
Speaker 1: Japan were the ones that were stealing the record back
Speaker 1: from between each other. And then China came out with
Speaker 1: a computer which I'm sure I'm gonna mispronounced because I
Speaker 1: I don't know how to pronounce Chinese, but tian hey
Speaker 1: is how it would be spelled in English, and and
Speaker 1: someone's probably gonna say it's sheen hey or something like that,
Speaker 1: if you please let us know, yeah, because I don't.
Speaker 1: But it was a computer from China that could run
Speaker 1: at two point five ped falops and uh it had
Speaker 1: fourteen thousand, three hundred thirty six Intel Xeon X five
Speaker 1: six seven zero CPUs and seven thousand, one D sixty
Speaker 1: eight in video Tesla GPUs and so that was, you know,
Speaker 1: a really impressive machine. That was that that stole all
Speaker 1: the titles away in But also another important moment for
Speaker 1: China in that year was that China developed the sun Way,
Speaker 1: which was slow by super comp Peter standards because they
Speaker 1: could only run a pedal flop UM and they had
Speaker 1: already gotten up to two point five pedal flops. Pedaphlop
Speaker 1: is still incredibly fast people, I'm just slow in general
Speaker 1: terms here relative terms. But the cool thing about the Sunway,
Speaker 1: at least from China's perspective, is that it was the
Speaker 1: first supercomputer China had designed with all Chinese processors, so
Speaker 1: they weren't depending upon some other companies process or some
Speaker 1: other country processors. They wanted to be able to be
Speaker 1: self reliant when it came to developing computers. And so
Speaker 1: that China really pushed it's it's computer engineering industry and
Speaker 1: was able to design you know, the Chinese UM engineers
Speaker 1: were able to design this the supercomputer UM. Then you
Speaker 1: had Fujitsu's K supercomputer, which until recently held the record.
Speaker 1: It was capable of running up to ten peda flops
Speaker 1: with eighty eight thousand one spark sixty war processors, and
Speaker 1: each CPU had sixteen gigabytes of local RAM, and it
Speaker 1: had one thousand, three hundred seventy seven terabytes of memory,
Speaker 1: and eventually it got up to seven five thousand process records. Yeah,
Speaker 1: it sits in Japan's REKN Advanced Institute for Computational Science.
Speaker 1: It sits in it thinks what And that's funny. It's
Speaker 1: asky only spelled in different. I mean the letters are
Speaker 1: in different. Um anyway, sorry, I just noticed that as
Speaker 1: I was looking down in my notes. Um, that's actually
Speaker 1: sort of why we decided to do this now, because
Speaker 1: it was just the week that we're recording this that
Speaker 1: we found out about the test. Now, they do these
Speaker 1: tests twice a year. Every six months, they have the
Speaker 1: top five hundred supercomputer sites. Um, so computers from all
Speaker 1: over the world. Uh. They put them on wheels at
Speaker 1: the top of this big hill and push it down
Speaker 1: the hill. It's like a big computer soapbox derby, you know.
Speaker 1: They uh they give them problems to solve and uh
Speaker 1: see who's the fastest the top five hundred supercomputers in
Speaker 1: the world, which, in a way it's kind of silly,
Speaker 1: but at the same time, very very cool and you
Speaker 1: can actually see the results of this if you want to,
Speaker 1: if you go to top five hundred dot org. Um.
Speaker 1: There there are the organizations that put it on uh
Speaker 1: publishers every year and that happens to be the University
Speaker 1: of Mannheim, Lawrence Berkeley National Laboratory, and the University of
Speaker 1: Tennessee actually do this and they are trying to figure
Speaker 1: out the the fastest, and the fastest was just announced.
Speaker 1: The new fastest was just announced this week, and we
Speaker 1: thought that would be a great time to talk about it.
Speaker 1: It's a machine actually name for a tree. Yes, it
Speaker 1: is the IBM Sequoia. And uh when when we say
Speaker 1: recording this week, the date is June. And so the
Speaker 1: Sequoia has taken the title of fastest supercomputer, which means
Speaker 1: that that's from IBMS, means the USA has the title
Speaker 1: once more, at least until the next Supercomputer Olympics. And um, yeah,
Speaker 1: it's a giant gold medal that is stamped on the
Speaker 1: outside of them. So you're you're probably all asking, hey,
Speaker 1: so what are some stats on this? Uh, the Sequoia computer,
Speaker 1: How how fast can it go? And what what's making
Speaker 1: it take? Well? I do want to point out that
Speaker 1: it is owned by the Department of Energy. UM, so
Speaker 1: this isn't really a military machine. UM. It is at
Speaker 1: the Lawrence Livermore National Laboratory. UM. And yes, the specs
Speaker 1: on this are pretty impressive. I mean it uses seven
Speaker 1: thousand kilowatts. Yeah, it's actually fairly efficient for a supercomputer. Yeah.
Speaker 1: It has one million, five hundred seventy two thousand, eight
Speaker 1: hundred sixty four processors and one point six peta bytes
Speaker 1: of memory. It takes up three thousand, four hundred twenty
Speaker 1: two square feet of space, so we've finally gotten back
Speaker 1: to that those enormous computers. Remember the stretch was two
Speaker 1: thousand square feet. Now this one's three thousand foo square feet. Uh.
Speaker 1: And it can run at sixteen point three two pedaphlops,
Speaker 1: so six point three to pedaphalops faster. Well, not even
Speaker 1: quite that much, because the K eventually got up to
Speaker 1: ten point five, but it is significantly faster than the K.
Speaker 1: So IBM now holds the the distinction of having the
Speaker 1: fastest or having designed the fastest supercomputer in the world. Now,
Speaker 1: I thought it'd be kind of fun too to compare
Speaker 1: that to IBM's Watson computer. Because that made headlines last
Speaker 1: year when Watson was designed in part to compete against
Speaker 1: humans in a very human game. Because we've already talked
Speaker 1: about computers playing chess again humans, we've also talked about
Speaker 1: computers playing other games against humans. In fact, we did
Speaker 1: a whole episode about this particular computer. So IBMS Watson
Speaker 1: was designed to play in a game show Let's Make
Speaker 1: a Deal. So they called out Watson and you didn't
Speaker 1: know what was behind Well, it was it did have
Speaker 1: a dress on. No, it wasn't. It wasn't Let's make
Speaker 1: a Deal. It was Jeopardy and uh. And in Jeopardy,
Speaker 1: of course, you are given an answer. You have to
Speaker 1: come up with the appropriate question. And it's it's really
Speaker 1: tricky for a computer to do this because it's not
Speaker 1: just a matching game where you matching an answer to
Speaker 1: a question. You also have to take in context. Sometimes
Speaker 1: there's word play, sometimes there's a riddle. Um, it's a
Speaker 1: it's a lot more complicated than just question answer. Yeah,
Speaker 1: they they specifically wanted it to play a human game.
Speaker 1: They didn't alter the clues. They're actually clues on this show.
Speaker 1: If you've never seen it, Um, they give you the
Speaker 1: answer and they you are supposed to supply the question,
Speaker 1: and they use wordplay and and things in these clues,
Speaker 1: and they specifically want the IBM engineers specifically wanted it
Speaker 1: to play a human game to to test its natural
Speaker 1: language processing ability. Can it figure out what from context
Speaker 1: what it is you're talking about? And it did very well. Yeah,
Speaker 1: So what was powering the Watson if you want to
Speaker 1: compare it to say the Sequoia, Well, it had a
Speaker 1: it was using ninety IBM power, seven fifty servers in
Speaker 1: ten server racks, and it had sixteen terabytes of memory
Speaker 1: and two thousand eight D eight processors um so or
Speaker 1: processor cores I should say, not just processors, uh, and
Speaker 1: so two thousand that sounds like a lot. But then
Speaker 1: you compare that to the one million, five hundred sixty
Speaker 1: four processors that the Sequoia has and you realize that Watson,
Speaker 1: as far as supercomputers go, doesn't merit mention. It's that
Speaker 1: which again, Watson was this I'm for a very specific purpose,
Speaker 1: this whole natural language being able to recognize that, being
Speaker 1: able to come up with information. That's a very specialized computer.
Speaker 1: So it doesn't necessarily have to have this incredible by
Speaker 1: comparison processing speed and number crunching ability, which might be
Speaker 1: used for other very intensive tasks, so things like very
Speaker 1: very realistic simulations that kind of thing, and predictions. So
Speaker 1: I just wanted to compare that so that people could
Speaker 1: understand because Watson's one of those words that we've heard
Speaker 1: a lot about and we think of that as like
Speaker 1: a supercomputer. But really, if we define supercomputer as a
Speaker 1: computer that has is on that bleeding edge of what
Speaker 1: a computer is capable of doing, it does not it
Speaker 1: doesn't measure up. But when you talk about comparing the
Speaker 1: top five hundred or putting a computer in a chess
Speaker 1: match or in a game of jeopardy, Um, you know
Speaker 1: I was. I made the joke that it was a
Speaker 1: little silly, and yeah, you could. You could say that
Speaker 1: you're using a computer. You could be using it for
Speaker 1: scientific purposes or doing something, and instead you're you're taking
Speaker 1: time off to do something else. But really, um, it's
Speaker 1: nice that for one thing, people understand what it is
Speaker 1: it's a supercomputer is and can do. And also it's uh,
Speaker 1: it's a way to test out these machines and make
Speaker 1: them better. Um, you know, even like I was talking
Speaker 1: about the h the power used by the Sequoia machine,
Speaker 1: it's considerably more efficient than the K computer. UM. The
Speaker 1: seven seven thousand watts beats K's twelve thousand swats. So
Speaker 1: with every every time that they come out with a
Speaker 1: new supercomputer, it's more efficient. They find better ways to
Speaker 1: route instructions, UM, you know, and and they can make
Speaker 1: things smaller than than before. So you really do see
Speaker 1: the implications in in our our everyday computers because now
Speaker 1: we have multi corps processors in um, these everyday devices
Speaker 1: that we use. UM. You don't necessarily need that to
Speaker 1: write a letter or surf the internet, but it does
Speaker 1: make things faster and more efficient. Uh, computers are are
Speaker 1: more reliable. You see advances in operating systems that we
Speaker 1: use every day because UM, the things that they've found out,
Speaker 1: UM in the process of making these supercomputers. They find
Speaker 1: better ways to route instructions in a simpler computer. UM.
Speaker 1: And so it's really worth it to do these these
Speaker 1: tests and UH find out just what a computer can do. So,
Speaker 1: you know, having a challenge just for the fun of it.
Speaker 1: You know, I don't see that necessarily as a bad thing, UM,
Speaker 1: you know, especially when we can we can make advances
Speaker 1: and build on those for the next generation of machines.
Speaker 1: And just to kind of sum this up, I thought
Speaker 1: I would just kind of a fun fact. If you
Speaker 1: look at the top ten fastest supercomputers in the world,
Speaker 1: three of them are in the United States, two of
Speaker 1: them are in Germany, two of them are in China,
Speaker 1: and the other three are in Japan, Italy, and France.
Speaker 1: Uh So that's where you could find these these supercomputers.
Speaker 1: And what I think is even more interesting is, let's
Speaker 1: see one, two, three, four, five of them were made
Speaker 1: by IBM, and only one of them was made by Craig.
Speaker 1: Uh that one being the Jaguar or Jaguar would you prefer,
Speaker 1: which is another wide of the ones in the United States.
Speaker 1: So IBM is definitely dominating the supercomputer space now, even
Speaker 1: though not all of those computers are in the United States,
Speaker 1: but IBM developed five of them, so that's pretty impressive. Uh.
Speaker 1: I guess that kind of sums up our conversation around supercomputers.
Speaker 1: And guys, if you have any ideas for episodes that
Speaker 1: we should cover in the future, let us know. Send
Speaker 1: us an email. Our address is tech stuff at Discovery
Speaker 1: dot com, or let us know on Facebook or Twitter
Speaker 1: or handled. There's text uff hs W and Chris and
Speaker 1: I will talk to you again really soon for more
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