Speaker 1: Welcome to tex Stuff, a production of I Heart Radios,
Speaker 1: How Stuff Works. Hey there, and welcome to tech Stuff.
Speaker 1: I'm your host, Jonathan Strickland. I'm an executive producer with
Speaker 1: How Stuff Works and I Heart Radio and I love
Speaker 1: all things tech, and today we're going to look at
Speaker 1: a classic episode that aired back in July of two
Speaker 1: thousand twelve, July six to be precise, and it is
Speaker 1: titled tech Stuff looks at super Computers. This was a
Speaker 1: fun discussion to have with my former co host Chris
Speaker 1: Pallette because I, you know, grew up in an era
Speaker 1: of super computers and only had sort of a vague
Speaker 1: idea of what that term meant for many years. I'm
Speaker 1: sure there was a time when I was a kid
Speaker 1: where I thought of it as a computer that wore
Speaker 1: a cape. But as it turns out, it gets a
Speaker 1: little more complicated than that. Let's listen in 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, honestly, I would say supercomputer.
Speaker 1: Is a computer that can do a lot more calculations
Speaker 1: in a shorter period of time than the machines sitting
Speaker 1: on our desktop. Yeah. I think of it as sort
Speaker 1: of the bleeding edge of what a computer is capable
Speaker 1: of doing. Something that that still feel 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. Two thousand square feet. It costs thirteen million dollars, which,
Speaker 1: if you were to translate to today's cash, would be
Speaker 1: ninety one million dollars. It's a lot of money. 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. Craig. 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, there was early uh that was
Speaker 1: really the early application for supercomputers was you know, needing
Speaker 1: to 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. Yeah, typically, especially with the
Speaker 1: early supercomputers, they were really designed for very specialized computing,
Speaker 1: so not necessarily specialized from the ground up for a
Speaker 1: one particular type of computing, but they were They were
Speaker 1: not meant to be general computers. They were meant to
Speaker 1: do tip no admiral computers because they were the Navy,
Speaker 1: that's true. Uh No, they were. They were meant to
Speaker 1: do a specific task very very well, and that's that's
Speaker 1: all they were meant to do. Now, Craig had an
Speaker 1: interesting philosophy he said, and this is this is a
Speaker 1: quote from him. He said, anyone can build a fast CPU.
Speaker 1: That trick is to build a fast system and that
Speaker 1: was the secret to create creating the first supercomputer. He
Speaker 1: realized that if you created a processor that was really,
Speaker 1: really fast, that did not matter if it couldn't get
Speaker 1: the data it needed to execute operations upon fast enough.
Speaker 1: So he saw the need to create a system that
Speaker 1: would move data through very very quickly, not just processed data,
Speaker 1: but move it so that means it needs a lot
Speaker 1: of memory, it needs a very fast pathway from memory
Speaker 1: to processor. There are a lot of pieces that have
Speaker 1: to be put in place, and he saw this very
Speaker 1: early on, and so using that philosophy, he designed a
Speaker 1: computer back in nineteen two that was called the c
Speaker 1: d C six six hundred. Now CDC stands for Controlled
Speaker 1: Data Corporation. Yeah, um h E R A was taken
Speaker 1: over by Remington Randy UM. And that's, uh, that's the
Speaker 1: name I remember because you know, UH still remember a
Speaker 1: lot of those old machine names UM from stuff that
Speaker 1: I found in my uh dad's collection. Of course, he was,
Speaker 1: you know, a mechanical engineer UM before he retired, and
Speaker 1: you know, so he was interested in all kinds of machines.
Speaker 1: And I didn't know what I was looking at at
Speaker 1: the time, of course, you know, but they were all
Speaker 1: these UM science and computing magazines laying around and that
Speaker 1: name I recognized also UNSIS because Remington Rand became Unities
Speaker 1: UM and probably a lot more of our listeners are
Speaker 1: familiar with that name. But he partnered with William Norris
Speaker 1: to start Controlled Data Corporation UM back in nine seven
Speaker 1: UM and really at that point, the UNIVAC from Remington
Speaker 1: Rand and IBM were the computing companies. And you know,
Speaker 1: IBM has been the heavyweight for so long, but CDC
Speaker 1: was the first, uh you know, upstart to really make
Speaker 1: a dent in there, uh stranglehold on the industry. And
Speaker 1: Craig wanted to join CDC fairly early on, but apparently
Speaker 1: he was needed for a project UM that would not
Speaker 1: let him leave exactly what he wanted to. So once
Speaker 1: he did leave, that's when he designed the CDC sixty hundred,
Speaker 1: which was officially announced in nineteen sixty four, so designed
Speaker 1: in sixty two, announced two years later, and it was
Speaker 1: the first commercially successful supercomputer, with a price tag of
Speaker 1: between seven and eight million dollars, sometimes going up as
Speaker 1: high as ten million, depending upon the configuration that the
Speaker 1: customer wanted. UM. Now, in today's cash, that would equal
Speaker 1: about sixty million dollars, so thirty one million dollars cheaper
Speaker 1: in today's money than the Stretch computer, and it was
Speaker 1: actually much more powerful. It had four hundred thousand transistors
Speaker 1: and one hundred miles of wiring, and it was the
Speaker 1: size of about four filing cabinets, so it's also significantly
Speaker 1: smaller than the Stretch. Didn't take up two thousand square feet.
Speaker 1: The clock speed was around a hundred nanoseconds, and it
Speaker 1: had sixty five thousand sixty bit words of memory. So
Speaker 1: this is kind of an odd time in computing. We
Speaker 1: hadn't really settled on the thirty two sixty four bit
Speaker 1: kind of model. This was before that. Um. It also
Speaker 1: used six high speed drums as sort of a temporary
Speaker 1: storage area. It had a central storage that used magnetic tape,
Speaker 1: and it used the four trans sixty six compiler. UM
Speaker 1: the equivalent to today's machines means that it would have
Speaker 1: about a ten mega hurts processor. Yeah, well that could
Speaker 1: work up to forty mega hurts and speed. Well, it
Speaker 1: could do a three million floating point operations per second.
Speaker 1: Yeah those areas, Yeah, so three million, that would be
Speaker 1: a mega flop, three mega flops, right, so we're gonna
Speaker 1: get into lots of different flop terms later as well,
Speaker 1: and they get incredibly huge. Of course, you have to
Speaker 1: keep it cool because otherwise it breaks out into a
Speaker 1: flop sweat. And that's true. Uh, well, not the flop
Speaker 1: sweat part, but you do have to keep it cool.
Speaker 1: As we know electronics, when you're running electricity through them,
Speaker 1: one of the byproducts is heat, and heat, as it
Speaker 1: turns out, is not a great thing for electronic components.
Speaker 1: It can make stuff expand contacts can lose connections, so
Speaker 1: that stuff starts to malfunction. An entire system could shut down.
Speaker 1: So the CDC had a cooling system that was provided
Speaker 1: by a special chemical free only. Yeah, they used free
Speaker 1: on to cool the system. In fact, it was they
Speaker 1: would use free On for a while before finally having
Speaker 1: to switch to a different coolant because free on just
Speaker 1: wasn't efficient enough. Eventually, now at it was still doing
Speaker 1: the job. So Cray was also an innovator in another way.
Speaker 1: The stretch IBM stretched um was sort of a hybrid machine.
Speaker 1: They had transistors and vacuum tubes in it um and
Speaker 1: that's I think why one of the reasons why craze
Speaker 1: machines were smaller. The six 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 machine,
Speaker 1: and so it would take up a lot less space
Speaker 1: than the vacuum tubes. And I would imagine that based
Speaker 1: on my knowledge, my personal knowledge of vacuum tubes, might
Speaker 1: 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 an 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 C d C connection
Speaker 1: here is that Thomas Watson Jr. Which was IBM s C.
Speaker 1: He was IBM CEO at the time, wrote a famous
Speaker 1: memo that time too IBM employees, and he said, last
Speaker 1: week Controlled Data announced the six system. I understand that
Speaker 1: in the laboratory developing the system, there are only thirty
Speaker 1: four people, including the janitor. Of these fourteen our engineers
Speaker 1: and four our programmers. Contrasting this modest effort with our
Speaker 1: vast developmental activities, I failed to understand why we have
Speaker 1: lost our industry leadership position by letting someone else offer
Speaker 1: the world's most powerful computer. Craig's response was a reportedly, well,
Speaker 1: there's your problem. Essentially, Craig was saying that, you know,
Speaker 1: perhaps IBMS approach it was a little burdened by size.
Speaker 1: That IBM had grown so large that to manage a
Speaker 1: project like this was very difficult to do because it
Speaker 1: was just too big. So that's an interesting idea that
Speaker 1: an organization needed to be kind of small and nim
Speaker 1: bowl in order to pull something off like creating the
Speaker 1: world's fastest computer. He followed up the c d C
Speaker 1: six hundred with the seventy, which had a sixty five thousand,
Speaker 1: five hundred thirty six sixty bit word memory and a
Speaker 1: clock speed of twenty seven nano seconds uh and actually
Speaker 1: in practice ran about five times faster than the sixty.
Speaker 1: But then Cray left c d C and he formed
Speaker 1: his own company, Kray Research, and in nineteen seventy six
Speaker 1: he introduced the Kray one, which if you've ever heard
Speaker 1: the Kray supercomputer, that's what this is. It's the crazy
Speaker 1: One was the first of those. It had a clock
Speaker 1: speed of a well, its processor ran at eighty mega
Speaker 1: hurts and back. At this time these supercomputers were still
Speaker 1: using a single CPU, so that was kind of interesting
Speaker 1: to these were single CPU systems. So it had eighty
Speaker 1: mega Hurts processor sixty four bit system. It ran at
Speaker 1: a hundred thirty six mega flops, so one or three
Speaker 1: six million floating operations per second, and it had one
Speaker 1: thousand six d sixty two printed circuit boards that made
Speaker 1: up the components of this computer. It costs between five
Speaker 1: and eight million dollars, depending on how you wanted it
Speaker 1: set up, and in today's cash that's about twenty five
Speaker 1: million dollars. So we see that the processor speed is
Speaker 1: increasing and the price is coming down. Often the size
Speaker 1: of the computer is decreasing as well, although that that
Speaker 1: also flip flops over the years because while the solid
Speaker 1: state electronics definitely brought the size down, eventually the way
Speaker 1: we pack in more speed requires more space. But we'll
Speaker 1: get into that. Okay. So after the Cray one came
Speaker 1: the Cray x MP. Yeah. This is uh, this is
Speaker 1: interesting because realized also in addition to the fact that
Speaker 1: he knew that the components, the all of the components
Speaker 1: the entire machine was important and not just a processor,
Speaker 1: he also realized that uh, early on that parallel processing
Speaker 1: could also speed things up. UM. Now it's common for
Speaker 1: us to have multi core processes in our desktop machines
Speaker 1: or laptops, or in fact, now we're starting to see
Speaker 1: them in our mobile devices. UM. But you know, at
Speaker 1: the at the time in the seventies and eighties, this
Speaker 1: was still something sort of newish, UM, and it's not
Speaker 1: something that everybody realized. Uh. So the x MP actually
Speaker 1: was to Cray one computers linked together, UM, and using
Speaker 1: those two machines together in a multiprocessing effort, UM, they
Speaker 1: could triple the performance of just one Cray one UM,
Speaker 1: which is something interesting to note. And uh Cray two
Speaker 1: had four processors in the same machine and that was
Speaker 1: the first to exceed one billion flops as Britainic it
Speaker 1: tells me. Yeah, uh, it actually could have up to
Speaker 1: eight CPUs the create too. UM. The these machines often
Speaker 1: over time were upgraded, so the initial step specs you
Speaker 1: would get when they were first released were one thing,
Speaker 1: and then by the end of the run of production
Speaker 1: they would be better. I mean, which makes sense. I
Speaker 1: mean we see that in computers all the time. We
Speaker 1: definitely we tend to call them different model numbers now,
Speaker 1: but the same sort of thing happens. So back in two,
Speaker 1: you had this the Create XMP with a hundred five
Speaker 1: mega hurts CPUs running around two hundred megaflops each. Uh,
Speaker 1: then if they had up to four CPUs you could
Speaker 1: get eight hundred megaflops going, and that was pretty impressive.
Speaker 1: It had the equivalent, by the way, of a hundred
Speaker 1: and twenty eight megabytes of RAM, So yeah, you think
Speaker 1: about that one or twenty eight megabytes of RAM in
Speaker 1: nineteen a D two that was considered bleeding edge for
Speaker 1: a supercomputer. Um and the storage units for the Cray
Speaker 1: XMP were the size of a file cabinet and they
Speaker 1: could hold up to twelve gigs of storage. Because they
Speaker 1: have a flash drive in my bag with me that
Speaker 1: has eight gigs. Yeah, and you can find you can
Speaker 1: find you just find twenty gig or more flash drives,
Speaker 1: which you know, you think about that, that's something that
Speaker 1: is small enough for you to carry on a key chain. Well,
Speaker 1: back in two you had a file cabinet sized device
Speaker 1: that could hold twelve gigs and that was considered massive,
Speaker 1: like a massive amount of information. So yeah, time really
Speaker 1: does change things, doesn't it. So, yeah, the Cray two.
Speaker 1: That's when they switched from free on to Flora Nert
Speaker 1: as their coolant. I'm sorry, but that sounds like a
Speaker 1: made up alien name from a from a an animated movie.
Speaker 1: Technically all names are made up. I know. That's I
Speaker 1: just blew your mind. What if there were no hypothetical questions?
Speaker 1: Turn So the Floria Nert. The reason why they switched
Speaker 1: was because they had at that point packed the components
Speaker 1: so tightly together that free on was not efficient enough
Speaker 1: to cool them. So they switched from free on to
Speaker 1: Flora Nert. And it's a little Floria Nert I've had
Speaker 1: around somewhere. And then they also had to figure out
Speaker 1: a new way to access the memory on the create too,
Speaker 1: because at this point they had reached that that point
Speaker 1: that Kray had mentioned earlier about creating a CPU I
Speaker 1: can process information faster than it can pull information in.
Speaker 1: So they found they would actually dedicate processors to getting
Speaker 1: data from memory and funneling it into the central processing units.
Speaker 1: And uh, this was this was really important. It was
Speaker 1: what kind of led the way into threading and loading memory. UH,
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 Cray 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 US that was they dominated that that
Speaker 1: country dominated the supercomputer industry. But in so this is
Speaker 1: you know, again, the Cray craze, if you will, lasted
Speaker 1: front the sixties all the way into the eighties. Well
Speaker 1: ninety six, Japan introduced the s R twenty two oh one,
Speaker 1: which had two thousand, forty eight processors. So remember a
Speaker 1: Cray too that was up to eight processors. The s
Speaker 1: R two two oh one two thousand forty eight processors. Yep,
Speaker 1: ye I count two thousand forty more processors with that computer.
Speaker 1: Then with the Cray, do my math could be off
Speaker 1: of an English major and it could it could have
Speaker 1: up to six hundred gigaflops of processing. That's kind of crazy. Um. Yeah.
Speaker 1: I also I also feel like we would be remissed
Speaker 1: to mention the efforts of Danny Hillis um W. Daniel
Speaker 1: Hillis was a grad student at m i T. Massachusetts
Speaker 1: Institute of Technology when he realized that distributing computing was
Speaker 1: the way of the future, if you will. Um. He
Speaker 1: started thinking Machines Corporation ine UM and this CM one,
Speaker 1: which was the first of his machines to come out
Speaker 1: in eight five m It had six one bit processors
Speaker 1: grouped sixteen to a chip. Interesting. Um, that's a that's
Speaker 1: a really interesting approach tiny tiny processors. Yeah, so you
Speaker 1: know wow. But yeah, I didn't come across my um my,
Speaker 1: my research, which is why this is actually really like
Speaker 1: I'm my mind is really as I'm thinking about that
Speaker 1: sort of architecture. That's really an interesting approach. Well, it's
Speaker 1: interesting too to see how different Uh see, Jonathan and
Speaker 1: I do our research separately on purpose so that we
Speaker 1: uh come up with different things on the cases and um,
Speaker 1: so it's funny that that I would have come across that. Also, well,
Speaker 1: I think of Danny hillis because I've seen his name
Speaker 1: a lot in things like a long Now Foundation and
Speaker 1: people with he hangs out with people like Stewart Brandon,
Speaker 1: Kevin Kelly, UM, fascinating people. But UM anyway, Yeah, that
Speaker 1: that's uh, that was one of his contributions. And you
Speaker 1: see that in again in today's machines. I mean we
Speaker 1: have this, you know, with us every day. But you
Speaker 1: know this is uh, this is when we started to
Speaker 1: realize that you don't necessarily have to go buy More's
Speaker 1: Law and wait until next year's chip comes out with
Speaker 1: twice as many processors on it. You can you can
Speaker 1: do this by dividing up the work. Yeah. And and
Speaker 1: in fact that that's another good point about the s
Speaker 1: R O one, the computer from Japan, because uh, in
Speaker 1: order to use these two thousand forty eight processors, there
Speaker 1: was a new development in computer science which was called
Speaker 1: multiple instruction multiple data or m I M D. Yes. Now,
Speaker 1: this is the idea of being able to solve problems
Speaker 1: by pulling in information from from memory and feeding it
Speaker 1: to different processors that are all using different operations on
Speaker 1: that data to come to a single solution, not necessarily
Speaker 1: a single solution, but that's I'm using that as as
Speaker 1: an example for this for this explanation. So this m
Speaker 1: I M D approach is what allowed us to develop
Speaker 1: multi core processors, because in this case we're still talking
Speaker 1: about single processors that are all grouped together. Eventually we
Speaker 1: will get to the point where we have multi core
Speaker 1: processors where a single processor has multiple cores and each
Speaker 1: core can work on part of a problem or separate
Speaker 1: problems to solve things faster, to to get to a
Speaker 1: conclusion faster than they would if it was just one
Speaker 1: single processor, even if it was a really really fast
Speaker 1: processor working on a series of problems. So I always,
Speaker 1: I always use this analogy. Imagine that you have one
Speaker 1: super smart math genius taking a math test, and the
Speaker 1: math genius is going through and solving all of these problems,
Speaker 1: and he or she is able to do this flawlessly,
Speaker 1: able to solve all the problems, but it takes a
Speaker 1: certain time to get through the test. Then you get
Speaker 1: that same test to four above average math students. They're
Speaker 1: not geniuses, but they're there. They can hold their own.
Speaker 1: And you divide it up, say all right, you take
Speaker 1: this this one fourth of the test. You take this quarter,
Speaker 1: you take this quarter, and you take that quarter and
Speaker 1: the four students together start to work. Those four students
Speaker 1: are very likely going to be able to finish the
Speaker 1: entirety of that test much faster, each of them working
Speaker 1: on a quarter of it, rather than the genius who
Speaker 1: is working on the full thing at the same time.
Speaker 1: Even though the genius is smarter and can work faster
Speaker 1: on each individual problem, collectively, those four students are going
Speaker 1: to solve that test faster. That's the philosophy behind both
Speaker 1: grouping cores together and making them a parallel processing unit
Speaker 1: or taking a multi core approach to a CPU. Yep,
Speaker 1: and you can you can thank Danny Hillis for figuring
Speaker 1: out the idea of massively parallel computing UM. But you
Speaker 1: know that that's a problem though too, because instead of
Speaker 1: having two machines running side by side and linked together,
Speaker 1: now you have to figure out how you're going to
Speaker 1: parse all those instructions between all those different processors. So
Speaker 1: you have to have the software or the operating system
Speaker 1: that will give instructions to each of the processors actively
Speaker 1: and direct essentially directing traffic. Yes, this is this is
Speaker 1: kind of like, it's not It's not a simple thing
Speaker 1: to figure out. It reminds me of Intel's tick talk
Speaker 1: approach to developing processors. You think of the tick being
Speaker 1: the physical machinery that's going to do the processing, and
Speaker 1: you think of the talk as the software that's optimized
Speaker 1: to work on that physical hardware to make it really
Speaker 1: live up to its potential. And then you have another
Speaker 1: tick where you've got an advancement in the physical hardware,
Speaker 1: but perhaps the last generation of software isn't really optimized
Speaker 1: to 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 computer that has self awareness.
Speaker 1: It's not necessarily that we can't reach the physical uh
Speaker 1: requirements we would need in order to have a computer
Speaker 1: be able to have some form of self awareness. It's
Speaker 1: the idea that we could throw as much horsepower at
Speaker 1: the problem as we wanted to. Without the software, it
Speaker 1: just won't happen. Will be rejoining this classic episode in
Speaker 1: just a moment, but first let's take a quick break
Speaker 1: to thank our sponsor. There's one company name we haven't
Speaker 1: really mentioned yet, and it's big. I mean, we talked
Speaker 1: about a little bit just then, but not in the
Speaker 1: terms of supercomputers. It's a big name in computer architecture,
Speaker 1: but it wasn't a really big name in the whole
Speaker 1: supercomputer story. And that's Intel. Now, Intel was not just
Speaker 1: sitting back during this whole time. Now, granted, Intel's main
Speaker 1: focus is on enterprise and consumer processors, which are not
Speaker 1: completely analogous to what is you you find in supercomputers
Speaker 1: at this time. Right, that would change, but not immediately.
Speaker 1: But Intel did develop something called the Paragon, which was
Speaker 1: supposed to be, you know, another fantastic supercomputer and it
Speaker 1: could support up to four thousand processors using this M
Speaker 1: I M D architecture. But it did not succeed in
Speaker 1: the market. It just sort of well, it flopped in
Speaker 1: a different way. The other kind of flopped, Yeah, the
Speaker 1: bad kind. So that didn't really go anywhere, but it
Speaker 1: did again sort of push this trend of parallel processing
Speaker 1: and M I M D. Uh. The Japanese came out
Speaker 1: with a couple of other computers called as Key Read
Speaker 1: and Asky White. Intel also had an askey read Um. Yeah. Well,
Speaker 1: actually this this goes back to the Comprehensive Test Band treaty. Uh,
Speaker 1: that the United States signed, Um, they needed a certification
Speaker 1: program for the nuclear weapons that they had built up
Speaker 1: and uh so what they started was the Accelerated Strategic
Speaker 1: Computing Initiative. Asking with only one eye instead of asking
Speaker 1: characters yes with two eyes, just to clarify yes, I'm
Speaker 1: glad you did, thank you, uh and ask you read yes.
Speaker 1: Was built at Sandy and National Laboratories in Albuquerque, New Mexico.
Speaker 1: Until helped them out with that, and that that was
Speaker 1: the first machine to get a terra flop. Yeah, and
Speaker 1: it was the first one to break the terra flop barrier.
Speaker 1: It did that with six thousand two d mega hurts
Speaker 1: pentium pro processors, nine thousand, 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 UM two z on processors. And
Speaker 1: it actually hit three point one tara flops at the
Speaker 1: end of its production life. So yeah, like I said,
Speaker 1: you know, when we give these numbers, there are different
Speaker 1: ones because there's a certain amount that was available when
Speaker 1: the computer first premiered. Then there was like the average
Speaker 1: amount during the computer's lifetime and then the amount that
Speaker 1: was available at the very end of its run time.
Speaker 1: So these numbers do change a little bit depending upon
Speaker 1: which source you're reading in which version of the computer
Speaker 1: they're looking at, because again, these computers are they come
Speaker 1: in a range of models, so not all of them
Speaker 1: are exactly the same. Now, while we talk about playing
Speaker 1: games like chess, you know that that's one of the
Speaker 1: big uh consumer uh visibility issues with supercomputers. You don't
Speaker 1: see what supercomputers do. And that was a way for them,
Speaker 1: the IBM in particular, to achieve notice, was taking on
Speaker 1: people like Gary Kasparov, chess masters worldwide with a supercomputer
Speaker 1: kind of computer outthink quote unquote out think a human. Well,
Speaker 1: the point of ASKI was again one of those behind
Speaker 1: the scenes thing. It was a very military thing. It
Speaker 1: was more like Whopper in more games actually, uh actually
Speaker 1: exactly 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 simulation,
Speaker 1: and uh so that's why they started the initiative. It
Speaker 1: was not a game, but a challenge. Hey, let's you know,
Speaker 1: let's keep coming up with newer faster machines because we
Speaker 1: need newer faster machines to run nuclear simulations and simulations
Speaker 1: in general were a big part of what these supercomputers
Speaker 1: were put to use for. I mean like climatology for example,
Speaker 1: weather predictions that was a big requirement as well, as
Speaker 1: supercomputers have been put towards that to try and help
Speaker 1: map and predict climate change and just weather patterns, not
Speaker 1: not just climate but weather, day to day weather, and
Speaker 1: also other simulations as well. Not to mention crunching data
Speaker 1: from facilities that generate lots and lots of information. So um,
Speaker 1: things like the SETI Institute would for extraterrestrial intelligence. Yes
Speaker 1: that they would use very powerful computers to try and
Speaker 1: crunch all the data they would get from radio telescopes.
Speaker 1: You also had things like the Large Hadron Collider and
Speaker 1: other super colliders that generate lots and lots of data
Speaker 1: and they need these really fast computers in order to
Speaker 1: process the data and make it meaningful. So UM. Moving on.
Speaker 1: So right around this time him when the sky Red
Speaker 1: comes out. Um, that's when there was a shift in supercomputing.
Speaker 1: So before there were all these customized UH computers that
Speaker 1: had their own processors or had thousands of processors running together. UH.
Speaker 1: But at this point it became possible to actually build
Speaker 1: a supercomputer with off the shelf parts. You could actually
Speaker 1: get enough computers together and linked them together to perform
Speaker 1: as a supercomputer. And this was also when there became
Speaker 1: a shift to using the Linux operating system. UH. So
Speaker 1: Lenox kind of replaces Unix as the OS of choice
Speaker 1: for people who are designing supercomputers, which is nice because
Speaker 1: now you can tell the company nurse never mind. In
Speaker 1: two thousand two, Japan comes back with the s key
Speaker 1: White where it's had a thirty five terra flops computer.
Speaker 1: It was the NYC Earth Simulator, and it cost a
Speaker 1: hair under a billion dollars nine million. It's a lot
Speaker 1: of hairs, actually hundred millions, a lot of hairs. If
Speaker 1: anyone wants to give me a hair in that sense,
Speaker 1: I will take it. Uh. And two thousand four, IBM
Speaker 1: comes out with the blue Gene slash L computer and
Speaker 1: had sixteen thousand computer nodes and each node had two CPUs.
Speaker 1: I'm gonna be thinking Bowie the rest of the day now,
Speaker 1: So yeah, thirty two thousand CPUs. Ultimately, if my math
Speaker 1: is correct, and then that could run at seventy terra flops,
Speaker 1: so twice as fast as the Asky White and a
Speaker 1: two thousand seven version of this could actually manage up
Speaker 1: to six hundred terra flops. And it had a hundred
Speaker 1: thousand computer nodes, so two hundred thousand processors. With that
Speaker 1: starting to get into some preretty ridiculous computers from you know,
Speaker 1: if you're looking at it as hey, I own a
Speaker 1: computer that's got a single processor, this one has a
Speaker 1: two hundred thousand of them. Yeah. Yeah. It also sort
Speaker 1: of uh makes apples claim. In the late nineties, UM
Speaker 1: sort of silly, UM, because well, the federal government classified
Speaker 1: a supercomputer UM I can't remember exactly when it was.
Speaker 1: It was in the late nineties and uh as as
Speaker 1: a machine that would run a giga flop and um
Speaker 1: IBM when they were still running on on power process
Speaker 1: power PC processors. UM, there was a MAC that they
Speaker 1: advertised as being a supercomputer because it could reach a
Speaker 1: gigga flop UM, and I just thought at the time
Speaker 1: it was kind of weird to think about UM, But
Speaker 1: now it's just kind of silly when you take it
Speaker 1: into context. And these these actual supercomputers at the time. Now, uh, yeah,
Speaker 1: a gigga flop is good, but no, right, So a
Speaker 1: mega flop is a million floating operations per second, a
Speaker 1: giga flop is a billion floating operations per second, a
Speaker 1: tara flop is a trillion floating operations per second. Well,
Speaker 1: and then there's a peta, which is a quadrillion floating
Speaker 1: operations per second per second. Yeah, quadrillion. And the first
Speaker 1: supercomputer to hit that and break that barrier was another
Speaker 1: IBM machine, the road Runner, And uh it had twenty
Speaker 1: thousand CPUs and it was the first computer to break
Speaker 1: that pedophal up barrier. So one quadrillion floating operations per second.
Speaker 1: It's a serious machine. Chris and I will return to
Speaker 1: discussing supercomputers in just a moment. After this quick break.
Speaker 1: In two, there was an interesting development because China entered
Speaker 1: the supercomputer Fray. Now at this point it was really
Speaker 1: a battle down between the United States and Japan and
Speaker 1: Germany also has quite a few supercomputers as well. But
Speaker 1: but US and Japan were the ones that were stealing
Speaker 1: the record back from between each other. And then China
Speaker 1: came out with a computer which I'm sure I'm gonna
Speaker 1: mispronounced because I I don't know how to pronounce Chinese,
Speaker 1: but tian hey is how it would be spelled in English,
Speaker 1: and and someone's probably gonna say it's sheen hey or
Speaker 1: something like that. 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 pedal flops 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 six eight
Speaker 1: in video Tesla GPUs and so that was, you know,
Speaker 1: a really impressive machine that was that stole all the
Speaker 1: titles away in But also another important moment for China
Speaker 1: in that year was that China developed the sun Way,
Speaker 1: which was slow by supercomputer standards because they could only
Speaker 1: run a pedal flop um and they had already gotten
Speaker 1: up to two point five pedal flops. Penahlop is still
Speaker 1: incredibly fast, people, I'm just slow slow in general terms.
Speaker 1: 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 pedaphlops with
Speaker 1: eighty eight thousand, one eight Spark sixty four processors, and
Speaker 1: each CPU had sixteen gigabytes of local RAN and it
Speaker 1: had one thousand, three D seventy seven terab bytes of memory,
Speaker 1: and eventually it got up to a seven hundred five
Speaker 1: thousand process records. Yeah, it sits in Japan's ken Advanced
Speaker 1: Institute for Computational Science in it thinks what And that's funny.
Speaker 1: It's asky only spelled in different I mean the letters
Speaker 1: are in different UM anyway, Sorry, I just noticed that
Speaker 1: as I was looking down in my notes. Um, that's
Speaker 1: actually sort of why we decided to do this now,
Speaker 1: because it was just the week that we're recording this
Speaker 1: that we found out about the test. Now they do
Speaker 1: these tests twice a year, every six months. They have
Speaker 1: the top five hundred supercomputer sites, um so computers from
Speaker 1: all over the world. Uh. They put them on wheels
Speaker 1: at the top of this big hill and push it
Speaker 1: down the hill race hands like a big computer soapbox derby,
Speaker 1: you know. They they they give them problems to solve
Speaker 1: and uh see who's the fastest the top five hundred
Speaker 1: supercomputers in the world. Which 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 dot org. Um there
Speaker 1: there are the organizations that put it on uh publish
Speaker 1: this 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 there 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 IBM. So it means the USA has
Speaker 1: the title once more, at least until the next Supercomputer Olympics.
Speaker 1: And um, yeah, it's a giant gold medal that is
Speaker 1: stamped on the outside of them. So you're you're probably
Speaker 1: all asking, hey, so what are some stats on this, uh,
Speaker 1: the Sequoia computer. How how fast can it go? And
Speaker 1: what what's making it take? Well? I do want to
Speaker 1: point out that it is owned by the Department of Energy, UM,
Speaker 1: so this isn't really a military machine. UM 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 a enormous computers. Remember the stretch was
Speaker 1: two thousand square feet. Now this one's three thousand two
Speaker 1: square feet and it can run at sixteen point three
Speaker 1: two pedal flops, so six point three to pedophalops faster,
Speaker 1: well not even quite that much, because the the K
Speaker 1: eventually got up to ten point five, but it is
Speaker 1: significantly faster than the K. So IBM now holds the
Speaker 1: the distinction of having the fastest or having designed the
Speaker 1: fastest supercomputer in the world. Now, I thought it'd be
Speaker 1: kind of fun too to compare that to IBM's Watson computer,
Speaker 1: because that made headlines last year when Watson was designed
Speaker 1: in part to compete against humans in a very human game.
Speaker 1: Because we've already talked about computers playing chess against humans,
Speaker 1: we've also talked about computers playing other games against humans.
Speaker 1: In fact, we did a full episode about this particular computer.
Speaker 1: So i'd be Watson was designed to play in a
Speaker 1: game show Let's Make a Deal, So they called out
Speaker 1: Watson and if you didn't know what was behind, well
Speaker 1: it was it did have a dress on no it wasn't.
Speaker 1: It wasn't let's make a deal. It was jeopardy and uh.
Speaker 1: And in Jeopardy, of course you are given an answer.
Speaker 1: You have to come up with the appropriate question. And
Speaker 1: it's it's really tricky for a computer to do this
Speaker 1: because it's not just a matching game where you matching
Speaker 1: an answer to a question. You also have to take
Speaker 1: in context. Sometimes there's word play, sometimes there's a riddle. Um,
Speaker 1: it's a 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 in this show.
Speaker 1: If you've never seen it. Um, they give you the answer,
Speaker 1: and they you are supposed to supply the question and uh.
Speaker 1: They use wordplay and and things in these clues. And
Speaker 1: they specifically want the IBM engineers specifically need it to
Speaker 1: play a human game to to test its natural language
Speaker 1: processing ability. Can it figure out what from context what
Speaker 1: 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 fifties servers in
Speaker 1: ten server racks, and it had sixteen terabytes of memory
Speaker 1: and two thousand eight processors um so or processor cores
Speaker 1: I should say, not just processors, uh, and so two
Speaker 1: thousand that sounds like a lot, But then you compare
Speaker 1: that to the one million, five sixty four processors that
Speaker 1: the Sequoia has and you realize that Watson, as far
Speaker 1: as supercomputers go, doesn't merit mention. It's that which, again,
Speaker 1: Watson was designed for a very specific purpose, this whole
Speaker 1: natural language. Being able to recognize that and being able
Speaker 1: 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 it's nice
Speaker 1: that for one thing, people understand what it is, it's
Speaker 1: 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 thowts beats K's twelve thousand watts. So with
Speaker 1: every every time that they come out with a new supercomputer,
Speaker 1: it's more efficient. They find better ways to route instructions, UM,
Speaker 1: you know, and and they can make things smaller than
Speaker 1: than before. So you really do see the implications in
Speaker 1: in our our everyday computers because now we have multi
Speaker 1: core processors in UM these everyday devices that we use,
Speaker 1: UM you don't necessarily need that write a letter or
Speaker 1: serve the internet. But it does make things faster and
Speaker 1: more efficient. Uh, computers are are more reliable. You see
Speaker 1: advances in operating systems that we use every day because um,
Speaker 1: the things that they found out, um in the process
Speaker 1: of making these supercomputers. They find better ways to route
Speaker 1: instructions in a simpler computer. Um. And so it's really
Speaker 1: worth it to do these these tests and uh find
Speaker 1: out just what a computer can do. So, you know,
Speaker 1: having a challenge just for the fun of it. You know,
Speaker 1: 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: And that's it for this classic episode. I hope you
Speaker 1: guys enjoyed it again. Was recorded in two thousand twelve.
Speaker 1: We've had bigger and better supercomputers come out since then,
Speaker 1: and we've also seen the rise of graphics processing units
Speaker 1: that have largely supplanted supercomputers in many, but not all applications.
Speaker 1: I've done other episodes about that. You can search our
Speaker 1: archive if you want to see those. The way you
Speaker 1: do that is you pop on over to our website
Speaker 1: text stuff podcast dot com. We have the archive there.
Speaker 1: It is searchable, so you can look for specific episodes
Speaker 1: that cover, you know, specific topics. Maybe you don't see
Speaker 1: the topic you want. Maybe you do a search and
Speaker 1: nothing comes up. Well, then you can write me an
Speaker 1: email and suggest that topic to me. The addresses tech
Speaker 1: stuff at how stuff works dot com, where you can
Speaker 1: pop on over to Facebook or Twitter. We have to
Speaker 1: handle text stuff h s W at both of those.
Speaker 1: You can send us a suggestion that way as well.
Speaker 1: And I hope to talk to you again. Really sick Yeah.
Speaker 1: Text Stuff is a production of I Heart Radio's How
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