TechStuff Looks at Supercomputers

would you say? Well, if I hadn't already made the joke, I would have said it was a computer in the Cape and tights, But no, I'm honestly I would say supercomputer is a computer that can do a lot more calculations in a shorter period of time than the machines sitting on our desktop. Yeah. I think of it as sort of the bleeding edge of what a computer is

capable of doing. Something that that still feels fills a room, even though typical computers these days don't need to fill a room because it's that big, it still has that much computing power, right right, And the term comes from

the nineteen sixties and uh. In order to really kind of understand the the the span of this, I think I was going to talk a little bit about the last computer I could find that was a powerful computer that existed before people started talking about super computers, which was the IBM seventy thirty stretch. Yes, that was the one that was made with elastic. Yes, ye gain a couple of pounds. Your computer can still you know fit. It was Mr. Fantastic of the computer world. No, because

it was not a super computer. It took up two thousand square feet back in the day, this being the early sixties, Higger than my two thousand square feet. It cost thirteen million dollars, which if you were to translate to today's cash, would be ninety one million dollars. It's a lot of cash. So that was the fastest computer at the time until a fellow named Seymour Roger Craig showed up. I s Mr Craze. Yeah. Craig ends up being a big name in supercomputers, particularly in the sixties,

seventies and up to the mid eighties. That was the name in supercomputers. And he was working for a company called Engineering Research Associates or E r A, which actually grew out of a naval operation, um being the U. S. Navy, not belly Button. I was gonna you were looking at me like joke. No, No, not that it was a Navy project. How about that as in as in the military force, not the color It was a Navy project

that was all about code breaking, all right. So there was this project about code breaking that eventually kind of spun off and became an actual company all on its own called Engineering Research Associates, and it branched out beyond code breaking, although it took all the code breaking work

it could get. Yeah, we talked about the Enigma UM some episodes back UM and we were talking about the bomb UM and yeah, those early uh that was really the early application for supercomputers was you know, needing to crunch a lot of data very quickly, and there weren't There weren't the kind of applications that we have now. We'll get into that, I'm sure in a few minutes, but but yeah, I mean that was you know, why

would you need a supercomputer? You know? That was That's probably about the only thing I could think of where people were needing to crunch that kind of information as quickly as possible and defense. Typically, especially with the early supercomputers, they were really designed for very specialized suting, so not necessarily specialized from the ground up for a one particular type of computing, but they were. They were not meant to be general computers. They were meant to do tip

no admiral computers, that's true. Uh No, they were. They were meant to do a specific task very very well. And that's that's all they were meant to do. Um. Now, Craig had an interesting philosophy, he said, and this is this is a quote from him. He said, anyone can build a fast CPU. The trick is to build a fast system. And that was the secret to Craig creating

the first supercomputer. He realized that if you created a processor that was really really fast, that did not matter if it couldn't get the data it needed to execute operations upon fast enough. So he saw the need to create a system that would move data through very very quickly, not just processed data, but move it so that means needs a lot of memory, It needs a very fast

pathway from memory to processor. There are a lot of pieces that have to be put in place, and he saw this very early on, and so using that philosophy, he designed a computer back in nineteen sixty two that was called the c d C sixty six hundred. Now

CDC stands for Controlled Data Corporation. Yeah, um, uh e R A was taken over by Remington Rand UM and that's uh, that's the name I remember because you know, UH still remember a lot of those old machine names UM from stuff that I found in my uh dad's collection.

Of course, he was, you know, a mechanical engineer UM before he retired, and you know, so he was interested in all kinds of machines and I didn't know what I was looking at at the time, of course, you know, but they were all these UM science and computing magazines laying around and that name I recognized also UNITIS because Remington Ran became Unities UM, and probably a lot more

of our listeners are familiar with that name. But he partnered with William Norris to start Controlled out of Corporation UM back in ninety seven. UM. And really at that point, the UNIVAC from Remington Rand and IBM were the computing companies. Yeah, you know, IBM has been the heavyweight for so long, but CDC was the first uh you know, upstart to really make a dent in there, uh stranglehold on the industry.

And and Craig wanted to join CDC fairly early on, but apparently he was needed for a project UM that would not let him leave exactly what he wanted to.

So once he did leave, that's when he designed the CDC sixty DRED, which was officially announced in nineteen sixty four, so designed in sixty two, announced two years later, and it was the first commercially successful supercomputer, with a price tag of between seven and eight million dollars, sometimes going up as high as ten million, depending upon the configuration

that the customer wanted. UM Now, in today's cash, that would equal about sixty million dollars, so thirty one million dollars cheaper in today's money than the Stretch computer, and it was actually much more powerful. It had four hundred thousand transistors and one hundred miles of wiring, and it was the size of about four filing cabinets, so it was also significantly smaller than the Stretch, didn't take up

two thousand square feet. The clock speed was around a hundred nanoseconds, and it had sixty five thousand sixty bit words of memory. So this is kind of an odd time in computing. We hadn't really settled on the thirty two sixty four bit kind of model. This was before that. UM. It also used six high speed drums as sort of a temporary storage area. It had a central storage that used magnetic tape, and it used the four trans sixty six compiler. UM the equivalent to today's machines means that

it would have about a ten mega hurts processor. Yeah, well that could work up to forty mega hurts and speed. Well, it could do a three million floating point operations per second. Yeah, so three million. That would be a mega flop. Three mega flops, right, So we're gonna get into lots of different flop terms later as well, and they get incredibly huge. H Of course, you have to keep it cool because

otherwise it breaks out into a flop sweat. That's true. Uh, well, not the flop sweat part, but you do have to keep it cool. As we know electronics, when you're running electricity through them, one of the byproducts is heat, and heat, as it turns out, is not a great thing for electronic components. It can make stuff expand contacts can lose connections, so that stuff starts to malfunction and entire system could shut down. So the CDC STRED had a cooling system

that was provided by a special chemical free on. Really, yeah, they used free on to cool the system. In fact, it was they would use free on for a while before finally having to switch to a different coolant because free on just wasn't efficient enough. Eventually, now at the hundred it was still doing the job. So Kari was also an innovator in another way. UM, the stretch IBM

stretched the UM was sort of a hybrid machine. They had transistors and vacuum tubes in it UM, and that's I think why one of the reasons why craze machines were smaller. The sixteen O four, which proceeded the sixty six hundred UM, was the one of the very first to use transistors only. So there was also a transistor machine and so it would take up a lot less space than the vacuum tubes. And I would imagine that based on my knowledge, my personal knowledge of vacuum tubes,

might have been a little cooler simply because of that. Yeah, I would imagine that they wouldn't have had to have as dramatic and a c system to keep the the room bearable because vacuum tubes do put off a lot of heat. Um Another interesting IBM CDC connection here is that Thomas Watson Jr. Which was IBM s C. He was IBM CEO at the time, wrote a famous memo that time two IBM employees, and he said, last week

Controlled Data announced the sixty system. I understand that in the laboratory developing the system, there are only thirty four people, including the janitor. Of these fourteen our engineers and for our programmers. Contrasting this modest effort with our vast developmental activities, I failed to understand why we have lost our industry leadership position by letting someone else offer the world's most

powerful computer. Craize's response was a reportedly, well, there's your problem. Essentially, Craig was saying that, you know, perhaps IBMS approach it was a little burdened by size that IBM had grown so large that to manage a project like this was very difficult to do because it was just too big. So that's an interesting idea that an organization needed to be kind of small and nimble in order to pull

something off like creating the world's fastest computer. He followed up the c d C sundred with which had a sixty five thousand, five hundred thirty six sixty bit word memory and a clock speed of twenty seven nano seconds uh and actually in practice ran about five times faster than the six. But then Cray left c d C and he formed his own company, Cray Research two and in nine six he introduced the Kray one, which if you've ever heard the craze supercomputer, that's what this is.

It's the crazy one was the first of those. It had a clock speed of a well, it's processor ran at eighty mega hurts and back at this time these supercomputers were still using a single CPU, so that was kind of interesting to these were single CPU systems. So it had eighty mega hurts processor, sixty four bit system, it ran at a hundred thirty six mega flops, so one or thirty six million floating operations per second, and it had one thousand, six hundred sixty two printed circuit

boards that made up the components of this computer. It costs between five and eight million dollars, depending on how you wanted it set up, and in today's cash, that's about twenty five million dollars. So we see that the processor speed is increasing and the price is coming down.

Often the size of the computer is decreasing as well, although that that also flip flops over the years because while the solid state electronics definitely brought the size down, eventually the way we pack in more speed requires more space.

But we'll get into that. Okay, So after the Cray one came the Cray x MP in Yeah, this is uh, this is interesting because Crai realized also in addition to the fact that he knew that the components, the all of the components, the entire machine was important and not just a processor, he also realized that, uh early on, that parallel processing could also speed things up. Um. Now it's common for us to have multi core processes in our desktop machines or laptops, or in fact, now we're

starting to see them in our mobile devices. Um. But you know, at the time in the seventies and eighties, this was still something sort of newish, um, and it's not something that everybody realized. Uh. So the x MP actually was to Cray one computers linked together um and using those two machines together in a multiprocessing effort UM they could triple the performance of just one Cray one UM,

which is something interesting to note. And uh, the Create two had four processors in the same machine, and that was the first to exceed one billion flops as Britainic it tells me. Yeah, uh, it actually could have up to eight CPUs c um. The these machines often over time were upgraded, so the initial step specs you would get when they were first released were one thing, and then by the end of the run of production they would be better. I mean, which makes sense. I mean

we see that in computers all the time. We definitely we tend to call them different model numbers now, but the same sort of thing happens. So back in two you had this Cray XMP with a hundred five mega hurts CPUs running around two hundred megaflops each. Uh, and if they had up to four CPUs you could get eight hundred megaflops going and that was pretty impressive and had the equivalent, by the way of a hundred and

twenty eight megabytes of RAM. So yeah, you think about that one or twenty eight megabytes of RAM in that was considered bleeding edge for a supercomputer. UM and the storage units for the Cray XMP were the size of a file cabinet and they could hold up to twelve gigs of storage because they have a flash in my

bag with me eight gigs. Yeah, and you can find, you can find, you can find twenty gig or more flash drives, which you know, you think about that, that's something that is small enough for you to carry on a key chain. While back in nineteen eighty two you had a file cabinet sized device that could hold twelve gigs and that was considered massive, like a massive amount of information. So, yeah, time really does change things, doesn't it. So yeah, the Cry two, that's when they switched from

free On to Flora Nert as their coolant. I'm sorry, but that sounds like a made up alien name from a from a an animated movie. Technically all names are made up. I know, that's I just blew your mind. What if there were no hypothetical questions? Turn on the yes.

So the Floria Nert the reason why they switched was because they had at that point packed the components so tightly together that free on was not efficient enough to cool them, so they switched from free on to Flora Nert and it's a little Floria Nert I've had around somewhere. Then they also had to figure a new way to access the memory on the Create too, because at this point they had reached that that point that Krey had mentioned earlier about creating a CPU that can process information

faster than it can pull information in. So they found they would actually dedicate processors to getting data from memory and funneling it into the central processing units. And UH, this was this was really important. It was what kind of led the way into threading and and loading memory CPUs that have that capability to load information from memory preloading things that kind of came out of this work.

In fact, a lot of the UH, the advances that we see in personal computers UM are really possible because of the pioneering work that was done in supercomputers. It was stuff that that found its way from the engineering of supercomputers into personal computers, often a completely different sense of scale, but a similar approach. Now after the Create too, that's when Japan started to produce some supercomputers that were UH that were actually faster than anything that was being

produced in the United States. So up until this point, it was all the US that was they dominated that, that country dominated the supercomputer industry. But in n six, so this is you know again, the Kray craze, if you will, lasted from the sixties all the way into the eighties. Well ninety six, Japan introduced the s R twenty two oh one, which had two thousand forty eight processors. So remember create two. That was up to eight processors. The s R two two oh one two thousand forty

eight processors. I count two thousand forty more processors with that computer. Then with the Cray, do my math could be off in an English major and it could it could have up to six hi flops of processing. That's kind of crazy. Um yeah. I also I also feel like we would be remissed to mention the efforts of Danny Hillis um W. Daniel Hillis was a grad student at m i T. Massachusetts Institute of Technology when he realized that distributing computing was the way of the future,

if you will. UM he started thinking Machines Corporation in UM and this CM one, which was the first of his machines to come out in eight five. Um it had sty six one bit processors grouped sixteen to a chip. Interesting, that's a that's a really interesting approach tiny tiny processors. Yeah, so you know, but yeah, I didn't come across that my um my, my research, which is why this is actually really like I'm my mind is really as I'm thinking about that sort of archetype. Sure, that's really an

interesting approach. Well, it's interesting too to see how different See Jonathan and I do our research separately on purpose so that we uh come up with different things on the cases. And um, so it's funny that that I would have come across that. Also. Well, I think of Danny hillis because I've seen his name a lot in things like along Now Foundation and people with he he hangs out with people like Stewart Brandon, Kevin Kelly, UM, fascinating people. But um anyway, yeah, that that's uh, that

was one of his contributions. And you see that in again in today's machines. I mean, we have this, you know, with us every day. But you know this is uh, this is when we started to realize that you don't necessarily have to go buy More's Law and wait until next year's chip comes out with twice as many processors on it. You can you can do this by dividing

up the work. Yeah, and and in fact, that's another good point about the SR two oh one, the computer from Japan, because, uh, in order to who use these two thousand forty eight processors, there was a new development in computer science which was called multiple instruction multiple data or m I m D. Now, this is the idea of being able to solve problems by pulling in information from from memory and feeding it to different processors that are all using different operations on that data to come

to a single solution, not necessarily a single solution, but that's I'm using that as as an example for this for this explanation. So this m I m D approach is what allowed us to develop multi core processors, because in this case we're still talking about single processors that

are all grouped together. Eventually we will get to the point where we have multi core processors where a single processor has multiple cores and each core can work on part of a problem or separate problems to solve things faster, to to get to a inclusion faster than they would if it was just one single processor, even if it was a really really fast processor working on a series of problems. So I always I always use this analogy.

Imagine that you have one super smart math genius taking a math test, and the math genius is going through and solving all of these problems, and he or she is able to do this flawlessly, able to solve all the problems, but it takes a certain amount of time to get through the test. Then you get that same test to four above average math students. They're not geniuses,

but they're there. They can hold their own. And you divide it up, say all right, you take this this one fourth of the test, you take this quarter, you take this quarter, and you take that quarter, and the four students together start to work. Those four students are very likely going to be able to finish the entirety of that test much faster, each of them working on a quarter of it, rather than the genius who is

working on the full thing at the same time. Even though the genius is smarter and can work faster on each individual problem, collectively those four students are going to solve that test faster. That's the philosophy behind both grouping cores together and making them a parallel processing unit or taking a multi core approach to a CPU. Yep, and you can. You can thank Danny Hillis for figuring out

the idea of massively parallel computing UM. But you know that that's a problem though too, because instead of having two machines running side by side and linked together, now you have to figure out how you're going to parse all those instructions between all those different processors. So you have to have the software or the operating system that will give instructions to each of the processors actively and direct essentially directing traffic. Yes, this is this is kind

of like, it's not. It's not a simple thing to figure out. It reminds me of Intel's to talk approach to developing processors. You think of the TICK being the physical machinery that's going to do the processing, and you think of the talk as the software that's optimized to work on that physical hardware to make it really live

up to its potential. And then you have another tick where you've got an advancement in the physical hardware, but perhaps the last generation of software isn't really optimized to work on that, so you have to make new software. This is a continuation. In fact, that's one of the things that people say is a barrier to artificial intelligence to the point of having a a computer that has

self awareness. It's not necessarily that we can't reach the physical uh requirements we would need in order to have a computer be able to have some form of self awareness. It's the idea that we could throw as much horsepower at the problem as we wanted to. Without the software, it just won't happen anyway. Get back to supercomputers specifically, there's one company name we haven't really mentioned yet, and it's big. I mean, we talked about a little bit

just then, but not in the terms of supercomputers. It's a big name in computer architecture, but it wasn't a really big name in the whole supercomputer story. And that's Intel. Now, Intel was not just sitting back during this whole time. Now, granted, Intel's main focus is on enterprise and consumer processors, which are not completely analogous to what is you you find in supercomputers at this time. Right, that would change, but

not immediately. But Intel did develop something called the Paragon, which was supposed to be, you know, another fantastic supercomputer, and it could support up to four thousand processors using this m I M D architecture. But it did not succeed in the market. It just sort of well, it lopped in a different way, the other kind of flawed, the bad kind, so that didn't really go anywhere, but it did again sort of push this trend of parallel

processing and m I M D. Uh. The Japanese came out with a couple of other computers called as Key Read and Asky White Until also had an Asky Read. Um. Yeah. Well actually this this goes back to the Comprehensive Test Band Treaty. Uh. The United States signed UM they needed a certification program for the nuclear weapons that they had built up and uh so what they started was the

Accelerated Strategic Computing Initiative. ASKI with only one eye instead of asking characters with two eyes, just to clarify, I'm glad you did, thank you, uh, and ask you Read Yes was built at Sandy and National Laboratories and No Albuquerque, New Mexico, UNTIL helped them out with that, and then that was the first machine to get a Tarra flop. Yeah, and it was the first one to break the Tarra

flop barrier. It did that with six thousand, two hundred mega hurts pentium pro processors, seventy two of them, well six thousand at first. It then eventually was upgraded. The very first one had six thousand and the very last one had nine thousand M two Xeon processors, and it actually hit three point one tarra flops at the end

of its production life. So yeah, like I said, you know, when we give these numbers, there are different ones because there's a certain amount that was available when the computer first premiered, then there was like the average amount during the computer's lifetime, and then the amount that was available

at the very end of its run time. So these numbers do change a little bit depending upon which source you're reading in which version of the computer they're looking at, because again, these computers are they come in a range of models, so not all of them are exactly the same. Now, while we talk about uh playing games like chess, you know that that's one of the big uh consumer UH visibility issues with supercomputer You don't see what supercomputers do.

And that was a way for them, the IBM, and in particular to achieve notice, was taking on people like Gary Kasparov chess masters worldwide with a supercomputer kind of computer outthink quote unquote out think a human. Well, the point of ASKI was again one of those behind the scenes thing. It was a very military thing. It was more like Whopper in more games. Actually, uh actually exactly

like that. The point was to simulate nuclear tests. Um. And that was why they needed a lot of computing power, uh and something a machine that could run a lot of calculations very quickly, because they wanted to, uh, you know, this is not something you want to do. Hey, well let's uh, let's test out fifty nuclear warheads. Yeah this, you know. They wanted to do this with a computer simulation and uh so that's why they started the initiative.

It was not a game, but a challenge. Hey let's you know, let's keep coming up with newer faster machines because we need newer faster machines to run nuclear simulations. Yeah. And simulations in general were a big part of what these supercomputers were put to use for. I mean like

climatology for example, weather predictions. That was a big requirement as well as supercomputers have been put towards that to try and help map and predict climate change and just weather patterns, not not just climate but weather day to day weather, and also other simulations as well. Not to mention crunching data from facilities that generate lots and lots of information. So um, things like the CET Institute would

for extraterrestrial intelligence. Yes that they would use very powerful computers to try and crunch all the data they would get from radio telescopes. You also had things like the Large Hadron Collider and other supercolliders that generate lots and lots of data and they need these really fast computers in order to process the data and make it meaningful. So um. Moving on. So right around this time when the asky read comes out, Um, that's when there was

a shift in supercomputing. So before there were all these customized uh computers that had their own processors or had thousands of processors running together. Uh. But at this point it became possible to actually build a supercomputer with off the shelf parts. You could actually get enough computers together and linked them together to perform as a supercomputer. And this was also when there became a shift to using

the Linux operating system. UH. So Lenox kind of replaces Unix as the OS of choice for people who are designing supercomputers, which is nice because now you can tell the company nurse never mind. In two thousand two, Japan comes back with the as Key White, where it's had a thirty five terra flops computer. It was the NBC Earth Simulator, and it cost a hair under a billion dollars million. It's a lot of hairs, actually millions, a lot of hairs. If anyone wants to give me a

hair in that sense, I will take it. Uh. And two thousand four, IBM comes out with the Blue Gene slash L computer and had sixteen thousand computer nodes and each node had two CPUs. I'm gonna be thinking Bowie the rest of the day now, So yeah, thirty two

thousand CPUs. Ultimately, if my math is correct, and then that could run it's seventy terra flops, so twice as fast as the Askey White, and a two thousand seven version of this could actually manage up to six hundred tarra flops and it had a hundred thousand computer nodes, so two hundred thousand processors. With that starting to get into some preretty ridiculous computers from you know if you're looking at it as, Hey, I own a computer that's got a single processor. This one has a two hundred

thousand of them. Yeah. Yeah. It also sort of UHUM makes apples claim. In the late nineties, UM sort of silly, UM, because well, the federal government classified a supercomputer UM. I can't remember exactly when it was, it was in the late nineties, and UH as as a machine that would run a giga flop and UM IBM when they were still running on power process power PC processors. UM, there was a Mac that they advertised as being a supercomputer

because it could reach a giga flop. UM. And I just thought at the time it was kind of weird to think about, UM, But now it's just kind of silly when you take it into context and these these actual supercomputers at the time. Uh, A gigga flop is good, but no, right, So a mega flop is a million floating operations per second, A gigga flop is a billion floating operations per second, A tarra flop is a trillion floating operations per second. Well, and which is a quadrillion

floating operations per second per second. Yeah, quadrillion and the first supercomputer to hit that and break that barrier was another IBM machine, the road Runner, and uh it had twenty thousand CPUs and it was the first computer to break that pedal flop barrier. So one quadrillion floating operations per second, it's a serious machine. In twous ten, there was an interesting development because China entered the supercomputer FRAY.

Now at this point it was really a battle down between the United States and Japan, and Germany also has quite a few supercomputers as well, but but US and Japan were the ones that were stealing the record back

from between each other. And then China came out with a computer which I'm sure I'm gonna mispronounced because I I don't know how to pronounce Chinese, but tian hey is how it would be spelled in English, and and someone's probably gonna say it's sheen hey or something like that, if you please let us know, yeah, because I don't.

But it was a computer from China that could run at two point five ped falops and uh it had fourteen thousand, three hundred thirty six Intel Xeon X five six seven zero CPUs and seven thousand, one D sixty eight in video Tesla GPUs and so that was, you know,

a really impressive machine. That was that that stole all the titles away in But also another important moment for China in that year was that China developed the sun Way, which was slow by super comp Peter standards because they could only run a pedal flop UM and they had already gotten up to two point five pedal flops. Pedaphlop is still incredibly fast people, I'm just slow in general

terms here relative terms. But the cool thing about the Sunway, at least from China's perspective, is that it was the first supercomputer China had designed with all Chinese processors, so they weren't depending upon some other companies process or some other country processors. They wanted to be able to be self reliant when it came to developing computers. And so that China really pushed it's it's computer engineering industry and was able to design you know, the Chinese UM engineers

were able to design this the supercomputer UM. Then you had Fujitsu's K supercomputer, which until recently held the record. It was capable of running up to ten peda flops with eighty eight thousand one spark sixty war processors, and each CPU had sixteen gigabytes of local RAM, and it had one thousand, three hundred seventy seven terabytes of memory, and eventually it got up to seven five thousand process records. Yeah, it sits in Japan's REKN Advanced Institute for Computational Science.

It sits in it thinks what And that's funny. It's asky only spelled in different. I mean the letters are in different. Um anyway, sorry, I just noticed that as I was looking down in my notes. Um, that's actually sort of why we decided to do this now, because it was just the week that we're recording this that we found out about the test. Now, they do these tests twice a year. Every six months, they have the top five hundred supercomputer sites. Um, so computers from all

over the world. Uh. They put them on wheels at the top of this big hill and push it down the hill. It's like a big computer soapbox derby, you know. They uh they give them problems to solve and uh see who's the fastest the top five hundred supercomputers in the world, which, in a way it's kind of silly, but at the same time, very very cool and you can actually see the results of this if you want to,

if you go to top five hundred dot org. Um. There there are the organizations that put it on uh publishers every year and that happens to be the University of Mannheim, Lawrence Berkeley National Laboratory, and the University of Tennessee actually do this and they are trying to figure out the the fastest, and the fastest was just announced. The new fastest was just announced this week, and we thought that would be a great time to talk about it. It's a machine actually name for a tree. Yes, it

is the IBM Sequoia. And uh when when we say recording this week, the date is June. And so the Sequoia has taken the title of fastest supercomputer, which means that that's from IBMS, means the USA has the title once more, at least until the next Supercomputer Olympics. And um, yeah, it's a giant gold medal that is stamped on the outside of them. So you're you're probably all asking, hey, so what are some stats on this? Uh, the Sequoia computer, How how fast can it go? And what what's making

it take? Well? I do want to point out that it is owned by the Department of Energy. UM, so this isn't really a military machine. UM. It is at the Lawrence Livermore National Laboratory. UM. And yes, the specs on this are pretty impressive. I mean it uses seven thousand kilowatts. Yeah, it's actually fairly efficient for a supercomputer. Yeah. It has one million, five hundred seventy two thousand, eight hundred sixty four processors and one point six peta bytes

of memory. It takes up three thousand, four hundred twenty two square feet of space, so we've finally gotten back to that those enormous computers. Remember the stretch was two thousand square feet. Now this one's three thousand foo square feet. Uh. And it can run at sixteen point three two pedaphlops,

so six point three to pedaphalops faster. Well, not even quite that much, because the K eventually got up to ten point five, but it is significantly faster than the K. So IBM now holds the the distinction of having the fastest or having designed the fastest supercomputer in the world. Now, I thought it'd be kind of fun too to compare that to IBM's Watson computer. Because that made headlines last year when Watson was designed in part to compete against

humans in a very human game. Because we've already talked about computers playing chess again humans, we've also talked about computers playing other games against humans. In fact, we did a whole episode about this particular computer. So IBMS Watson was designed to play in a game show Let's Make a Deal. So they called out Watson and you didn't know what was behind Well, it was it did have a dress on. No, it wasn't. It wasn't Let's make a Deal. It was Jeopardy and uh. And in Jeopardy,

of course, you are given an answer. You have to come up with the appropriate question. And it's it's really tricky for a computer to do this because it's not just a matching game where you matching an answer to a question. You also have to take in context. Sometimes there's word play, sometimes there's a riddle. Um, it's a it's a lot more complicated than just question answer. Yeah, they they specifically wanted it to play a human game.

They didn't alter the clues. They're actually clues on this show. If you've never seen it, Um, they give you the answer and they you are supposed to supply the question, and they use wordplay and and things in these clues, and they specifically want the IBM engineers specifically wanted it to play a human game to to test its natural language processing ability. Can it figure out what from context

what it is you're talking about? And it did very well. Yeah, So what was powering the Watson if you want to compare it to say the Sequoia, Well, it had a it was using ninety IBM power, seven fifty servers in ten server racks, and it had sixteen terabytes of memory and two thousand eight D eight processors um so or processor cores I should say, not just processors, uh, and

so two thousand that sounds like a lot. But then you compare that to the one million, five hundred sixty four processors that the Sequoia has and you realize that Watson, as far as supercomputers go, doesn't merit mention. It's that which again, Watson was this I'm for a very specific purpose, this whole natural language being able to recognize that, being able to come up with information. That's a very specialized computer.

So it doesn't necessarily have to have this incredible by comparison processing speed and number crunching ability, which might be used for other very intensive tasks, so things like very very realistic simulations that kind of thing, and predictions. So I just wanted to compare that so that people could understand because Watson's one of those words that we've heard a lot about and we think of that as like

a supercomputer. But really, if we define supercomputer as a computer that has is on that bleeding edge of what a computer is capable of doing, it does not it doesn't measure up. But when you talk about comparing the top five hundred or putting a computer in a chess match or in a game of jeopardy, Um, you know I was. I made the joke that it was a little silly, and yeah, you could. You could say that

you're using a computer. You could be using it for scientific purposes or doing something, and instead you're you're taking time off to do something else. But really, um, it's nice that for one thing, people understand what it is it's a supercomputer is and can do. And also it's uh, it's a way to test out these machines and make them better. Um, you know, even like I was talking about the h the power used by the Sequoia machine, it's considerably more efficient than the K computer. UM. The

seven seven thousand watts beats K's twelve thousand swats. So with every every time that they come out with a new supercomputer, it's more efficient. They find better ways to route instructions, UM, you know, and and they can make things smaller than than before. So you really do see the implications in in our our everyday computers because now we have multi corps processors in um, these everyday devices

that we use. UM. You don't necessarily need that to write a letter or surf the internet, but it does make things faster and more efficient. Uh, computers are are more reliable. You see advances in operating systems that we use every day because UM, the things that they've found out, UM in the process of making these supercomputers. They find better ways to route instructions in a simpler computer. UM. And so it's really worth it to do these these

tests and UH find out just what a computer can do. So, you know, having a challenge just for the fun of it. You know, I don't see that necessarily as a bad thing, UM, you know, especially when we can we can make advances and build on those for the next generation of machines. And just to kind of sum this up, I thought

I would just kind of a fun fact. If you look at the top ten fastest supercomputers in the world, three of them are in the United States, two of them are in Germany, two of them are in China, and the other three are in Japan, Italy, and France. Uh So that's where you could find these these supercomputers. And what I think is even more interesting is, let's see one, two, three, four, five of them were made by IBM, and only one of them was made by Craig.

Uh that one being the Jaguar or Jaguar would you prefer, which is another wide of the ones in the United States. So IBM is definitely dominating the supercomputer space now, even though not all of those computers are in the United States, but IBM developed five of them, so that's pretty impressive. Uh. I guess that kind of sums up our conversation around supercomputers. And guys, if you have any ideas for episodes that we should cover in the future, let us know. Send

us an email. Our address is tech stuff at Discovery dot com, or let us know on Facebook or Twitter or handled. There's text uff hs W and Chris and I will talk to you again really soon for more on this and thousands of other topics. Is it how staff works dot com brought to you by the reinvented two thousand twelve camera. It's ready, are you