>> Narrator: From around the globe. It's theCUBE with digital coverage of Exascale Day. Made possible by Hewlett Packard Enterprise. >> Welcome everyone to theCUBE's celebration of Exascale Day. Dr. Mark Fernandez is here. He's the HPC technology officer for the Americas at Hewlett Packard enterprise. And he's a developer of the spaceborne computer, which we're going to talk about today. Mark, welcome. It's great to see you. >> Great to be here. Thanks for having me. >> You're very welcome. So let's start with Exascale Day. It's on 10 18, of course, which is 10 to the power of 18. That's a one followed by 18 zeros. I joke all the time. It takes six commas to write out that number. (Mark laughing) But Mark, why don't we start? What's the significance of that number? >> So it's a very large number. And in general, we've been marking the progress of our computational capabilities in thousands. So exascale is a thousand times faster than where we are today. We're in an era today called the petaflop era which is 10 to the 15th. And prior to that, we were in the teraflop era, which is 10 to the 12th. I can kind of understand a 10 to the 12th and I kind of can discuss that with folks 'cause that's a trillion of something. And we know a lot of things that are in trillions, like our national debt, for example. (Dave laughing) But a billion, billion is an exascale and it will give us a thousand times more computational capability than we have in general today. >> Yeah, so when you think about going from terascale to petascale to exascale I mean, we're not talking about orders of magnitude, we're talking about a much more substantial improvement. And that's part of the reason why it's sort of takes so long to achieve these milestones. I mean, it kind of started back in the sixties and seventies and then... >> Yeah. >> We've been in the petascale now for more than a decade if I think I'm correct. >> Yeah, correct. We got there in 2007. And each of these increments is an extra comma, that's the way to remember it. So we want to add an extra comma and get to the exascale era. So yeah, like you say, we entered the current petaflop scale in 2007. Before that was the terascale, teraflop era and it was in 1997. So it took us 10 years to get that far, but it's taken us, going to take us 13 or 14 years to get to the next one. >> And we say flops, we're talking about floating point operations. And we're talking about the number of calculations that can be done in a second. I mean, talk about not being able to get your head around it, right? Is that's what talking about here? >> Correct scientists, engineers, weather forecasters, others use real numbers and real math. And that's how you want to rank those performance is based upon those real numbers times each other. And so that's why they're floating point numbers. >> When I think about supercomputers, I can't help but remember whom I consider the father of supercomputing Seymour Cray. Cray of course, is a company that Hewlett Packard Enterprise acquired. And he was kind of an eclectic fellow. I mean, maybe that's unfair but he was an interesting dude. But very committed to his goal of really building the world's fastest computers. When you look at back on the industry, how do you think about its developments over the years? >> So one of the events that stands out in my mind is I was working for the Naval Research Lab outside Stennis Space Center in Mississippi. And we were doing weather modeling. And we got a Cray supercomputer. And there was a party when we were able to run a two week prediction in under two weeks. So the scientists and engineers had the math to solve the problem, but the current computers would take longer than just sitting and waiting and looking out the window to see what the weather was like. So when we can make a two week prediction in under two weeks, there was a celebration. And that was in the eighties, early nineties. And so now you see that we get weather predictions in eight hours, four hours and your morning folks will get you down to an hour. >> I mean, if you think about the history of super computing it's really striking to consider the challenges in the efforts as we were just talking about, I mean, decade plus to get to the next level. And you see this coming to fruition now, and we're saying exascale likely 2021. So what are some of the innovations in science, in medicine or other areas you mentioned weather that'll be introduced as exascale computing is ushered in, what should people expect? >> So we kind of alluded to one and weather affects everybody, everywhere. So we can get better weather predictions, which help everybody every morning before you get ready to go to work or travel or et cetera. And again, storm predictions, hurricane predictions, flood predictions, the forest fire predictions, those type things affect everybody, everyday. Those will get improved with exascale. In terms of medicine, we're able to take, excuse me, we're able to take genetic information and attempt to map that to more drugs quicker than we have in the past. So we'll be able to have drug discovery happening much faster with an exascale system out there. And to some extent that's happening now with COVID and all the work that we're doing now. And we realize that we're struggling with these current computers to find these solutions as fast as everyone wants them. And exascale computers will help us get there much faster in the future in terms of medicine. >> Well, and of course, as you apply machine intelligence and AI and machine learning to the applications running on these supercomputers, that just takes it to another level. I mean, people used to joke about you can't predict the weather and clearly we've seen that get much, much better. Now it's going to be interesting to see with climate change. That's another wildcard variable but I'm assuming the scientists are taking that into consideration. I mean, actually been pretty accurate about the impacts of climate change, haven't they? >> Yeah, absolutely. And the climate change models will get better with exascale computers too. And hopefully we'll be able to build a confidence in the public and the politicians in those results with these better, more powerful computers. >> Yeah let's hope so. Now let's talk about the spaceborne computer and your involvement in that project. Your original spaceborne computer it went up on a SpaceX reusable rocket. Destination of course, was the international space station. Okay, so what was the genesis of that project and what was the outcome? So we were approached by a long time customer NASA Ames. And NASA Ames says its mission is to model rocket launches and space missions and return to earth. And they had the foresight to realize that their supercomputers here on earth, could not do that mission when we got to Mars. And so they wanted to plan ahead and they said, "Can you take a small part of our supercomputer today and just prove that it can work in space? And if it can't figure out what we need to do to make it work, et cetera." So that's what we did. We took identical hardware, that's present at NASA Ames. We put it on a SpaceX rocket no special preparations for it in terms of hardware or anything of that sort, no special hardening, because we want to take the latest technology just before we head to Mars with us. I tell people you wouldn't want to get in the rocket headed to Mars with a flip phone. You want to take the latest iPhone, right? And all of the computers on board, current spacecrafts are about the 2007 era that we were talking about, in that era. So we want to take something new with us. We got the spaceone computer on board. It was installed in the ceiling because in space, there's no gravity. And you can put computers in the ceiling. And we immediately made a computer run. And we produced a trillion calculations a second which got us into the teraflop range. The first teraflop in space was pretty exciting. >> Well, that's awesome. I mean, so this is the ultimate example of edge computing. >> Yes. You mentioned you wanted to see if it could work and it sounds like it did. I mean, there was obviously a long elapse time to get it up and running 'cause you have to get it up there. But it sounds like once you did, it was up and running very quickly so it did work. But what were some of the challenges that you encountered maybe some of the learnings in terms of getting it up and running? >> So it's really fascinating. Astronauts are really cool people but they're not computer scientists, right? So they see a cord, they see a place to plug it in, they plug it in and of course we're watching live on the video and you plugged it in the wrong spot. So (laughs) Mr. Astronaut, can we back up and follow the procedure more carefully and get this thing plugged in carefully. They're not computer technicians used to installing a supercomputer. So we were able to get the system packaged for the shake, rattle and roll and G-forces of launch in the SpaceX. We were able to give astronaut instructions on how to install it and get it going. And we were able to operate it here from earth and get some pretty exciting results. >> So our supercomputers are so easy to install even an astronaut can do it. I don't know. >> That's right. (both laughing) Here on earth we have what we call a customer replaceable units. And we had to replace a component. And we looked at our instructions that are tried and true here on earth for average Joe, a customer to do that and realized without gravity, we're going to have to update this procedure. And so we renamed it an astronaut replaceable unit and it worked just fine. >> Yeah, you can't really send an SE out to space to fix it, can you? >> No sir. (Dave laughing) You have to have very careful instructions for these guys but they're great. It worked out wonderfully. >> That's awesome. Let's talk about spaceborne two. Now that's on schedule to go back to the ISS next year. What are you trying to accomplish this time? >> So in retrospect, spaceborne one was a proof of concept. Can we package it up to fit on SpaceX? Can we get the astronauts to install it? And can we operate it from earth? And if so, how long will it last? And do we get the right answers? 100% mission success on that. Now spaceborne two is, we're going to release it to the community of scientists, engineers and space explorers and say, "Hey this thing is rock solid, it's proven. Come use it to improve your edge computing." We'd like to preserve the network downlink bandwidth for all that imagery, all that genetic data, all that other data and process it on the edge as the whole world is moving to now. Don't move the data, let's compute at the edge and that's what we're going to do with spaceborne two. And so what's your expectation for how long the project is going to last? What does success look like in your mind? So spaceborne one was given a one year mission just to see if we could do it but the idea then was planted it's going to take about three years to get to Mars and back. So if you're successful, let's see if this computer can last three years. And so we're going up February 1st, if we go on schedule and we'll be up two to three years and as long as it works, we'll keep computing and computing on the edge. >> That's amazing. I mean, I feel like, when I started the industry, it was almost like there was a renaissance in supercomputing. You certainly had Cray and you had all these other companies, you remember thinking machines and convex spun out tried to do a mini supercomputer. And you had, really a lot of venture capital and then things got quiet for a while. I feel like now with all this big data and AI, we're seeing in all the use cases that you talked about, we're seeing another renaissance in supercomputing. I wonder if you could give us your final thoughts. >> Yeah, absolutely. So we've got the generic like you said, floating point operations. We've now got specialized image processing processors and we have specialized graphics processing units, GPUs. So all of the scientists and engineers are looking at these specialized components and bringing them together to solve their missions at the edge faster than ever before. So there's heterogeneity of computing is coming together to make humanity a better place. And how are you going to celebrate Exascale Day? You got to special cocktail you going to shake up or what are you going to do? It's five o'clock somewhere on 10 18, and I'm a Parrothead fan. So I'll probably have a margarita. There you go all right. Well Mark, thanks so much for sharing your thoughts on Exascale Day. Congratulations on your next project, the spaceborne two. Really appreciate you coming to theCUBE. Thank you very much I've enjoyed it. All right, you're really welcome. And thank you for watching everybody. Keep it right there. This is Dave Vellante for thecUBE. We're celebrating Exascale Day. We'll be right back. (upbeat music)

>>from around the globe. It's the Q. With digital coverage >>of exa scale day made possible by Hewlett Packard Enterprise. We're back at the celebration of Exa Scale Day. This is Dave Volant, and I'm pleased to welcome to great guests Brian Dance Berries Here. Here's what The ISS Program Science office at the Johnson Space Center. And Dr Mark Fernandez is back. He's the Americas HPC technology officer at Hewlett Packard Enterprise. Gentlemen, welcome. >>Thank you. Yeah, >>well, thanks for coming on. And, Mark, Good to see you again. And, Brian, I wonder if we could start with you and talk a little bit about your role. A T. I s s program Science office as a scientist. What's happening these days? What are you working on? >>Well, it's been my privilege the last few years to be working in the, uh, research integration area of of the space station office. And that's where we're looking at all of the different sponsors NASA, the other international partners, all the sponsors within NASA, and, uh, prioritizing what research gets to go up to station. What research gets conducted in that regard. And to give you a feel for the magnitude of the task, but we're coming up now on November 2nd for the 20th anniversary of continuous human presence on station. So we've been a space faring society now for coming up on 20 years, and I would like to point out because, you know, as an old guy myself, it impresses me. That's, you know, that's 25% of the US population. Everybody under the age of 20 has never had a moment when they were alive and we didn't have people living and working in space. So Okay, I got off on a tangent there. We'll move on in that 20 years we've done 3000 experiments on station and the station has really made ah, miraculously sort of evolution from, ah, basic platform, what is now really fully functioning national lab up there with, um, commercially run research facilities all the time. I think you can think of it as the world's largest satellite bus. We have, you know, four or five instruments looking down, measuring all kinds of things in the atmosphere during Earth observation data, looking out, doing astrophysics, research, measuring cosmic rays, X ray observatory, all kinds of things, plus inside the station you've got racks and racks of experiments going on typically scores, you know, if not more than 50 experiments going on at any one time. So, you know, the topic of this event is really important. Doesn't NASA, you know, data transmission Up and down, all of the cameras going on on on station the experiments. Um, you know, one of one of those astrophysics observatory's you know, it has collected over 15 billion um uh, impact data of cosmic rays. And so the massive amounts of data that that needs to be collected and transferred for all of these experiments to go on really hits to the core. And I'm glad I'm able toe be here and and speak with you today on this. This topic. >>Well, thank you for that, Bryan. A baby boomer, right? Grew up with the national pride of the moon landing. And of course, we've we've seen we saw the space shuttle. We've seen international collaboration, and it's just always been something, you know, part of our lives. So thank you for the great work that you guys were doing their mark. You and I had a great discussion about exa scale and kind of what it means for society and some of the innovations that we could maybe expect over the coming years. Now I wonder if you could talk about some of the collaboration between what you guys were doing and Brian's team. >>Uh, yeah, so yes, indeed. Thank you for having me early. Appreciate it. That was a great introduction. Brian, Uh, I'm the principal investigator on Space Born computer, too. And as the two implies, where there was one before it. And so we worked with Bryant and his team extensively over the past few years again high performance computing on board the International Space Station. Brian mentioned the thousands of experiments that have been done to date and that there are currently 50 orm or going on at any one time. And those experiments collect data. And up until recently, you've had to transmit that data down to Earth for processing. And that's a significant amount of bandwidth. Yeah, so with baseball and computer to we're inviting hello developers and others to take advantage of that onboard computational capability you mentioned exa scale. We plan to get the extra scale next year. We're currently in the era that's called PETA scale on. We've been in the past scale era since 2000 and seven, so it's taken us a while to make it that next lead. Well, 10 years after Earth had a PETA scale system in 2017 were able to put ah teraflop system on the International space station to prove that we could do a trillion calculations a second in space. That's where the data is originating. That's where it might be best to process it. So we want to be able to take those capabilities with us. And with H. P. E. Acting as a wonderful partner with Brian and NASA and the space station, we think we're able to do that for many of these experiments. >>It's mind boggling you were talking about. I was talking about the moon landing earlier and the limited power of computing power. Now we've got, you know, water, cool supercomputers in space. I'm interested. I'd love to explore this notion of private industry developing space capable computers. I think it's an interesting model where you have computer companies can repurpose technology that they're selling obviously greater scale for space exploration and apply that supercomputing technology instead of having government fund, proprietary purpose built systems that air. Essentially, you use case, if you will. So, Brian, what are the benefits of that model? The perhaps you wouldn't achieve with governments or maybe contractors, you know, kind of building these proprietary systems. >>Well, first of all, you know, any any tool, your using any, any new technology that has, you know, multiple users is going to mature quicker. You're gonna have, you know, greater features, greater capabilities, you know, not even talking about computers. Anything you're doing. So moving from, you know, governor government is a single, um, you know, user to off the shelf type products gives you that opportunity to have things that have been proven, have the technology is fully matured. Now, what had to happen is we had to mature the space station so that we had a platform where we could test these things and make sure they're gonna work in the high radiation environments, you know, And they're gonna be reliable, because first, you've got to make sure that that safety and reliability or taken care of so that that's that's why in the space program you're gonna you're gonna be behind the times in terms of the computing power of the equipment up there because, first of all and foremost, you needed to make sure that it was reliable and say, Now, my undergraduate degree was in aerospace engineering and what we care about is aerospace engineers is how heavy is it, how big and bulky is it because you know it z expensive? You know, every pound I once visited Gulfstream Aerospace, and they would pay their employees $1000 that they could come up with a way saving £1 in building that aircraft. That means you have more capacity for flying. It's on the orders of magnitude. More important to do that when you're taking payloads to space. So you know, particularly with space born computer, the opportunity there to use software and and check the reliability that way, Uh, without having to make the computer, you know, radiation resistance, if you will, with heavy, you know, bulky, um, packaging to protect it from that radiation is a really important thing, and it's gonna be a huge advantage moving forward as we go to the moon and on to Mars. >>Yeah, that's interesting. I mean, your point about cots commercial off the shelf technology. I mean, that's something that obviously governments have wanted to leverage for a long, long time for many, many decades. But but But Mark the issue was always the is. Brian was just saying the very stringent and difficult requirements of space. Well, you're obviously with space Born one. You got to the point where you had visibility of the economics made sense. It made commercial sense for companies like Hewlett Packard Enterprise. And now we've sort of closed that gap to the point where you're sort of now on that innovation curve. What if you could talk about that a little bit? >>Yeah, absolutely. Brian has some excellent points, you know, he said, anything we do today and requires computers, and that's absolutely correct. So I tell people that when you go to the moon and when you go to the Mars, you probably want to go with the iPhone 10 or 11 and not a flip phone. So before space born was sent up, you went with 2000 early two thousands computing technology there which, like you said many of the people born today weren't even around when the space station began and has been occupied so they don't even know how to program or use that type of computing. Power was based on one. We sent the exact same products that we were shipping to customers today, so they are current state of the art, and we had a mandate. Don't touch the hardware, have all the protection that you can via software. So that's what we've done. We've got several philosophical ways to do that. We've implemented those in software. They've been successful improving in the space for one, and now it's space born to. We're going to begin the experiments so that the rest of the community so that the rest of the community can figure out that it is economically viable, and it will accelerate their research and progress in space. I'm most excited about that. Every venture into space as Brian mentioned will require some computational capability, and HP has figured out that the economics air there we need to bring the customers through space ball into in order for them to learn that we are reliable but current state of the art, and that we could benefit them and all of humanity. >>Guys, I wanna ask you kind of a two part question. And, Brian, I'll start with you and it z somewhat philosophical. Uh, I mean, my understanding was and I want to say this was probably around the time of the Bush administration w two on and maybe certainly before that, but as technology progress, there was a debate about all right, Should we put our resource is on moon because of the proximity to Earth? Or should we, you know, go where no man has gone before and or woman and get to Mars? Where What's the thinking today, Brian? On that? That balance between Moon and Mars? >>Well, you know, our plans today are are to get back to the moon by 2024. That's the Artemus program. Uh, it's exciting. It makes sense from, you know, an engineering standpoint. You take, you know, you take baby steps as you continue to move forward. And so you have that opportunity, um, to to learn while you're still, you know, relatively close to home. You can get there in days, not months. If you're going to Mars, for example, toe have everything line up properly. You're looking at a multi year mission you know, it may take you nine months to get there. Then you have to wait for the Earth and Mars to get back in the right position to come back on that same kind of trajectory. So you have toe be there for more than a year before you can turn around and come back. So, you know, he was talking about the computing power. You know, right now that the beautiful thing about the space station is, it's right there. It's it's orbiting above us. It's only 250 miles away. Uh, so you can test out all of these technologies. You can rely on the ground to keep track of systems. There's not that much of a delay in terms of telemetry coming back. But as you get to the moon and then definitely is, you get get out to Mars. You know, there are enough minutes delay out there that you've got to take the computing power with you. You've got to take everything you need to be able to make those decisions you need to make because there's not time to, um, you know, get that information back on the ground, get back get it back to Earth, have people analyze the situation and then tell you what the next step is to do. That may be too late. So you've got to think the computing power with you. >>So extra scale bring some new possibilities. Both both for, you know, the moon and Mars. I know Space Born one did some simulations relative. Tomorrow we'll talk about that. But But, Brian, what are the things that you hope to get out of excess scale computing that maybe you couldn't do with previous generations? >>Well, you know, you know, market on a key point. You know, bandwidth up and down is, of course, always a limitation. In the more computing data analysis you can do on site, the more efficient you could be with parsing out that that bandwidth and to give you ah, feel for just that kind of think about those those observatory's earth observing and an astronomical I was talking about collecting data. Think about the hours of video that are being recorded daily as the astronauts work on various things to document what they're doing. They many of the biological experiments, one of the key key pieces of data that's coming back. Is that video of the the microbes growing or the plants growing or whatever fluid physics experiments going on? We do a lot of colloids research, which is suspended particles inside ah liquid. And that, of course, high speed video. Is he Thio doing that kind of research? Right now? We've got something called the I s s experience going on in there, which is basically recording and will eventually put out a syriza of basically a movie on virtual reality recording. That kind of data is so huge when you have a 360 degree camera up there recording all of that data, great virtual reality, they There's still a lot of times bringing that back on higher hard drives when the space six vehicles come back to the Earth. That's a lot of data going on. We recorded videos all the time, tremendous amount of bandwidth going on. And as you get to the moon and as you get further out, you can a man imagine how much more limiting that bandwidth it. >>Yeah, We used to joke in the old mainframe days that the fastest way to get data from point a to Point B was called C Tam, the Chevy truck access method. Just load >>up a >>truck, whatever it was, tapes or hard drive. So eso and mark, of course space born to was coming on. Spaceport one really was a pilot, but it proved that the commercial computers could actually work for long durations in space, and the economics were feasible. Thinking about, you know, future missions and space born to What are you hoping to accomplish? >>I'm hoping to bring. I'm hoping to bring that success from space born one to the rest of the community with space born to so that they can realize they can do. They're processing at the edge. The purpose of exploration is insight, not data collection. So all of these experiments begin with data collection. Whether that's videos or samples are mold growing, etcetera, collecting that data, we must process it to turn it into information and insight. And the faster we can do that, the faster we get. Our results and the better things are. I often talk Thio College in high school and sometimes grammar school students about this need to process at the edge and how the communication issues can prevent you from doing that. For example, many of us remember the communications with the moon. The moon is about 250,000 miles away, if I remember correctly, and the speed of light is 186,000 miles a second. So even if the speed of light it takes more than a second for the communications to get to the moon and back. So I can remember being stressed out when Houston will to make a statement. And we were wondering if the astronauts could answer Well, they answered as soon as possible. But that 1 to 2 second delay that was natural was what drove us crazy, which made us nervous. We were worried about them in the success of the mission. So Mars is millions of miles away. So flip it around. If you're a Mars explorer and you look out the window and there's a big red cloud coming at you that looks like a tornado and you might want to do some Mars dust storm modeling right then and there to figure out what's the safest thing to do. You don't have the time literally get that back to earth have been processing and get you the answer back. You've got to take those computational capabilities with you. And we're hoping that of these 52 thousands of experiments that are on board, the SS can show that in order to better accomplish their missions on the moon. And Omar, >>I'm so glad you brought that up because I was gonna ask you guys in the commercial world everybody talks about real time. Of course, we talk about the real time edge and AI influencing and and the time value of data I was gonna ask, you know, the real time, Nous, How do you handle that? I think Mark, you just answered that. But at the same time, people will say, you know, the commercial would like, for instance, in advertising. You know, the joke the best. It's not kind of a joke, but the best minds of our generation tryingto get people to click on ads. And it's somewhat true, unfortunately, but at any rate, the value of data diminishes over time. I would imagine in space exploration where where you're dealing and things like light years, that actually there's quite a bit of value in the historical data. But, Mark, you just You just gave a great example of where you need real time, compute capabilities on the ground. But but But, Brian, I wonder if I could ask you the value of this historic historical data, as you just described collecting so much data. Are you? Do you see that the value of that data actually persists over time, you could go back with better modeling and better a i and computing and actually learn from all that data. What are your thoughts on that, Brian? >>Definitely. I think the answer is yes to that. And, you know, as part of the evolution from from basically a platform to a station, we're also learning to make use of the experiments in the data that we have there. NASA has set up. Um, you know, unopened data access sites for some of our physical science experiments that taking place there and and gene lab for looking at some of the biological genomic experiments that have gone on. And I've seen papers already beginning to be generated not from the original experimenters and principal investigators, but from that data set that has been collected. And, you know, when you're sending something up to space and it to the space station and volume for cargo is so limited, you want to get the most you can out of that. So you you want to be is efficient as possible. And one of the ways you do that is you collect. You take these earth observing, uh, instruments. Then you take that data. And, sure, the principal investigators air using it for the key thing that they designed it for. But if that data is available, others will come along and make use of it in different ways. >>Yeah, So I wanna remind the audience and these these these air supercomputers, the space born computers, they're they're solar powered, obviously, and and they're mounted overhead, right? Is that is that correct? >>Yeah. Yes. Space borne computer was mounted in the overhead. I jokingly say that as soon as someone could figure out how to get a data center in orbit, they will have a 50 per cent denser data station that we could have down here instead of two robes side by side. You can also have one overhead on. The power is free. If you can drive it off a solar, and the cooling is free because it's pretty cold out there in space, so it's gonna be very efficient. Uh, space borne computer is the most energy efficient computer in existence. Uh, free electricity and free cooling. And now we're offering free cycles through all the experimenters on goal >>Eso Space born one exceeded its mission timeframe. You were able to run as it was mentioned before some simulations for future Mars missions. And, um and you talked a little bit about what you want to get out of, uh, space born to. I mean, are there other, like, wish list items, bucket bucket list items that people are talking about? >>Yeah, two of them. And these air kind of hypothetical. And Brian kind of alluded to them. Uh, one is having the data on board. So an example that halo developers talk to us about is Hey, I'm on Mars and I see this mold growing on my potatoes. That's not good. So let me let me sample that mold, do a gene sequencing, and then I've got stored all the historical data on space borne computer of all the bad molds out there and let me do a comparison right then and there before I have dinner with my fried potato. So that's that's one. That's very interesting. A second one closely related to it is we have offered up the storage on space borne computer to for all of your raw data that we process. So, Mr Scientist, if if you need the raw data and you need it now, of course, you can have it sent down. But if you don't let us just hold it there as long as they have space. And when we returned to Earth like you mentioned, Patrick will ship that solid state disk back to them so they could have a new person, but again, reserving that network bandwidth, uh, keeping all that raw data available for the entire duration of the mission so that it may have value later on. >>Great. Thank you for that. I want to end on just sort of talking about come back to the collaboration between I S s National Labs and Hewlett Packard Enterprise, and you've got your inviting project ideas using space Bourne to during the upcoming mission. Maybe you could talk about what that's about, and we have A We have a graphic we're gonna put up on DSM information that you can you can access. But please, mark share with us what you're planning there. >>So again, the collaboration has been outstanding. There. There's been a mention off How much savings is, uh, if you can reduce the weight by a pound. Well, our partners ice s national lab and NASA have taken on that cost of delivering baseball in computer to the international space station as part of their collaboration and powering and cooling us and giving us the technical support in return on our side, we're offering up space borne computer to for all the onboard experiments and all those that think they might be wanting doing experiments on space born on the S s in the future to take advantage of that. So we're very, very excited about that. >>Yeah, and you could go toe just email space born at hp dot com on just float some ideas. I'm sure at some point there'll be a website so you can email them or you can email me david dot volonte at at silicon angle dot com and I'll shoot you that that email one or that website once we get it. But, Brian, I wanna end with you. You've been so gracious with your time. Uh, yeah. Give us your final thoughts on on exa scale. Maybe how you're celebrating exa scale day? I was joking with Mark. Maybe we got a special exa scale drink for 10. 18 but, uh, what's your final thoughts, Brian? >>Uh, I'm going to digress just a little bit. I think I think I have a unique perspective to celebrate eggs a scale day because as an undergraduate student, I was interning at Langley Research Center in the wind tunnels and the wind tunnel. I was then, um, they they were very excited that they had a new state of the art giant room size computer to take that data we way worked on unsteady, um, aerodynamic forces. So you need a lot of computation, and you need to be ableto take data at a high bandwidth. To be able to do that, they'd always, you know, run their their wind tunnel for four or five hours. Almost the whole shift. Like that data and maybe a week later, been ableto look at the data to decide if they got what they were looking for? Well, at the time in the in the early eighties, this is definitely the before times that I got there. They had they had that computer in place. Yes, it was a punchcard computer. It was the one time in my life I got to put my hands on the punch cards and was told not to drop them there. Any trouble if I did that. But I was able thio immediately after, uh, actually, during their run, take that data, reduce it down, grabbed my colored pencils and graph paper and graph out coefficient lift coefficient of drag. Other things that they were measuring. Take it back to them. And they were so excited to have data two hours after they had taken it analyzed and looked at it just pickled them. Think that they could make decisions now on what they wanted to do for their next run. Well, we've come a long way since then. You know, extra scale day really, really emphasizes that point, you know? So it really brings it home to me. Yeah. >>Please, no, please carry on. >>Well, I was just gonna say, you know, you talked about the opportunities that that space borne computer provides and and Mark mentioned our colleagues at the I S s national lab. You know, um, the space station has been declared a national laboratory, and so about half of the, uh, capabilities we have for doing research is a portion to the national lab so that commercial entities so that HP can can do these sorts of projects and universities can access station and and other government agencies. And then NASA can focus in on those things we want to do purely to push our exploration programs. So the opportunities to take advantage of that are there marks opening up the door for a lot of opportunities. But others can just Google S s national laboratory and find some information on how to get in the way. Mark did originally using s national lab to maybe get a good experiment up there. >>Well, it's just astounding to see the progress that this industry is made when you go back and look, you know, the early days of supercomputing to imagine that they actually can be space born is just tremendous. Not only the impacts that it can have on Space six exploration, but also society in general. Mark Wayne talked about that. Guys, thanks so much for coming on the Cube and celebrating Exa scale day and helping expand the community. Great work. And, uh, thank you very much for all that you guys dio >>Thank you very much for having me on and everybody out there. Let's get the XO scale as quick as we can. Appreciate everything you all are >>doing. Let's do it. >>I've got a I've got a similar story. Humanity saw the first trillion calculations per second. Like I said in 1997. And it was over 100 racks of computer equipment. Well, space borne one is less than fourth of Iraq in only 20 years. So I'm gonna be celebrating exa scale day in anticipation off exa scale computers on earth and soon following within the national lab that exists in 20 plus years And being on Mars. >>That's awesome. That mark. Thank you for that. And and thank you for watching everybody. We're celebrating Exa scale day with the community. The supercomputing community on the Cube Right back

>>from around the globe. It's the Q with digital coverage of exa scale day made possible by Hewlett Packard Enterprise. Welcome, everyone to the Cube celebration of Exa Scale Day. Shaheen Khan is here. He's the founding partner, an analyst at Orion X And, among other things, he is the co host of Radio free HPC Shaheen. Welcome. Thanks for coming on. >>Thanks for being here, Dave. Great to be here. How are you >>doing? Well, thanks. Crazy with doing these things, Cove in remote interviews. I wish we were face to face at us at a supercomputer show, but, hey, this thing is working. We can still have great conversations. And And I love talking to analysts like you because you bring an independent perspective. You're very wide observation space. So So let me, Like many analysts, you probably have sort of a mental model or a market model that you look at. So maybe talk about your your work, how you look at the market, and we could get into some of the mega trends that you see >>very well. Very well. Let me just quickly set the scene. We fundamentally track the megatrends of the Information Age And, of course, because we're in the information age, digital transformation falls out of that. And the megatrends that drive that in our mind is Ayotte, because that's the fountain of data five G. Because that's how it's gonna get communicated ai and HBC because that's how we're gonna make sense of it Blockchain and Cryptocurrencies because that's how it's gonna get transacted on. That's how value is going to get transferred from the place took place and then finally, quantum computing, because that exemplifies how things are gonna get accelerated. >>So let me ask you So I spent a lot of time, but I D. C and I had the pleasure of of the High Performance computing group reported into me. I wasn't an HPC analyst, but over time you listen to those guys, you learning. And as I recall, it was HPC was everywhere, and it sounds like we're still seeing that trend where, whether it was, you know, the Internet itself were certainly big data, you know, coming into play. Uh, you know, defense, obviously. But is your background mawr HPC or so that these other technologies that you're talking about it sounds like it's your high performance computing expert market watcher. And then you see it permeating into all these trends. Is that a fair statement? >>That's a fair statement. I did grow up in HPC. My first job out of school was working for an IBM fellow doing payroll processing in the old days on and and And it went from there, I worked for Cray Research. I worked for floating point systems, so I grew up in HPC. But then, over time, uh, we had experiences outside of HPC. So for a number of years, I had to go do commercial enterprise computing and learn about transaction processing and business intelligence and, you know, data warehousing and things like that, and then e commerce and then Web technology. So over time it's sort of expanded. But HPC is a like a bug. You get it and you can't get rid of because it's just so inspiring. So supercomputing has always been my home, so to say >>well and so the reason I ask is I wanted to touch on a little history of the industry is there was kind of a renaissance in many, many years ago, and you had all these startups you had Kendall Square Research Danny Hillis thinking machines. You had convex trying to make many supercomputers. And it was just this This is, you know, tons of money flowing in and and then, you know, things kind of consolidate a little bit and, uh, things got very, very specialized. And then with the big data craze, you know, we've seen HPC really at the heart of all that. So what's your take on on the ebb and flow of the HPC business and how it's evolved? >>Well, HBC was always trying to make sense of the world, was trying to make sense of nature. And of course, as much as we do know about nature, there's a lot we don't know about nature and problems in nature are you can classify those problems into basically linear and nonlinear problems. The linear ones are easy. They've already been solved. The nonlinear wants. Some of them are easy. Many of them are hard, the nonlinear, hard, chaotic. All of those problems are the ones that you really need to solve. The closer you get. So HBC was basically marching along trying to solve these things. It had a whole process, you know, with the scientific method going way back to Galileo, the experimentation that was part of it. And then between theory, you got to look at the experiment and the data. You kind of theorize things. And then you experimented to prove the theories and then simulation and using the computers to validate some things eventually became a third pillar of off science. On you had theory, experiment and simulation. So all of that was going on until the rest of the world, thanks to digitization, started needing some of those same techniques. Why? Because you've got too much data. Simply, there's too much data to ship to the cloud. There's too much data to, uh, make sense of without math and science. So now enterprise computing problems are starting to look like scientific problems. Enterprise data centers are starting to look like national lab data centers, and there is that sort of a convergence that has been taking place gradually, really over the past 34 decades. And it's starting to look really, really now >>interesting, I want I want to ask you about. I was like to talk to analysts about, you know, competition. The competitive landscape is the competition in HPC. Is it between vendors or countries? >>Well, this is a very interesting thing you're saying, because our other thesis is that we are moving a little bit beyond geopolitics to techno politics. And there are now, uh, imperatives at the political level that are driving some of these decisions. Obviously, five G is very visible as as as a piece of technology that is now in the middle of political discussions. Covert 19 as you mentioned itself, is a challenge that is a global challenge that needs to be solved at that level. Ai, who has access to how much data and what sort of algorithms. And it turns out as we all know that for a I, you need a lot more data than you thought. You do so suddenly. Data superiority is more important perhaps than even. It can lead to information superiority. So, yeah, that's really all happening. But the actors, of course, continue to be the vendors that are the embodiment of the algorithms and the data and the systems and infrastructure that feed the applications. So to say >>so let's get into some of these mega trends, and maybe I'll ask you some Colombo questions and weaken geek out a little bit. Let's start with a you know, again, it was one of this when I started the industry. It's all it was a i expert systems. It was all the rage. And then we should have had this long ai winter, even though, you know, the technology never went away. But But there were at least two things that happened. You had all this data on then the cost of computing. You know, declines came down so so rapidly over the years. So now a eyes back, we're seeing all kinds of applications getting infused into virtually every part of our lives. People trying to advertise to us, etcetera. Eso So talk about the intersection of AI and HPC. What are you seeing there? >>Yeah, definitely. Like you said, I has a long history. I mean, you know, it came out of MIT Media Lab and the AI Lab that they had back then and it was really, as you mentioned, all focused on expert systems. It was about logical processing. It was a lot of if then else. And then it morphed into search. How do I search for the right answer, you know, needle in the haystack. But then, at some point, it became computational. Neural nets are not a new idea. I remember you know, we had we had a We had a researcher in our lab who was doing neural networks, you know, years ago. And he was just saying how he was running out of computational power and we couldn't. We were wondering, you know what? What's taking all this difficult, You know, time. And it turns out that it is computational. So when deep neural nets showed up about a decade ago, arm or it finally started working and it was a confluence of a few things. Thalib rhythms were there, the data sets were there, and the technology was there in the form of GPS and accelerators that finally made distractible. So you really could say, as in I do say that a I was kind of languishing for decades before HPC Technologies reignited it. And when you look at deep learning, which is really the only part of a I that has been prominent and has made all this stuff work, it's all HPC. It's all matrix algebra. It's all signal processing algorithms. are computational. The infrastructure is similar to H B. C. The skill set that you need is the skill set of HPC. I see a lot of interest in HBC talent right now in part motivated by a I >>mhm awesome. Thank you on. Then I wanna talk about Blockchain and I can't talk about Blockchain without talking about crypto you've written. You've written about that? I think, you know, obviously supercomputers play a role. I think you had written that 50 of the top crypto supercomputers actually reside in in China A lot of times the vendor community doesn't like to talk about crypto because you know that you know the fraud and everything else. But it's one of the more interesting use cases is actually the primary use case for Blockchain even though Blockchain has so much other potential. But what do you see in Blockchain? The potential of that technology And maybe we can work in a little crypto talk as well. >>Yeah, I think 11 simple way to think of Blockchain is in terms off so called permission and permission less the permission block chains or when everybody kind of knows everybody and you don't really get to participate without people knowing who you are and as a result, have some basis to trust your behavior and your transactions. So things are a lot calmer. It's a lot easier. You don't really need all the supercomputing activity. Whereas for AI the assertion was that intelligence is computer herbal. And with some of these exa scale technologies, we're trying to, you know, we're getting to that point for permission. Less Blockchain. The assertion is that trust is computer ble and, it turns out for trust to be computer ble. It's really computational intensive because you want to provide an incentive based such that good actors are rewarded and back actors. Bad actors are punished, and it is worth their while to actually put all their effort towards good behavior. And that's really what you see, embodied in like a Bitcoin system where the chain has been safe over the many years. It's been no attacks, no breeches. Now people have lost money because they forgot the password or some other. You know, custody of the accounts have not been trustable, but the chain itself has managed to produce that, So that's an example of computational intensity yielding trust. So that suddenly becomes really interesting intelligence trust. What else is computer ble that we could do if we if we had enough power? >>Well, that's really interesting the way you described it, essentially the the confluence of crypto graphics software engineering and, uh, game theory, Really? Where the bad actors air Incentive Thio mined Bitcoin versus rip people off because it's because because there are lives better eso eso so that so So Okay, so make it make the connection. I mean, you sort of did. But But I want to better understand the connection between, you know, supercomputing and HPC and Blockchain. We know we get a crypto for sure, like in mind a Bitcoin which gets harder and harder and harder. Um and you mentioned there's other things that we can potentially compute on trust. Like what? What else? What do you thinking there? >>Well, I think that, you know, the next big thing that we are really seeing is in communication. And it turns out, as I was saying earlier, that these highly computational intensive algorithms and models show up in all sorts of places like, you know, in five g communication, there's something called the memo multi and multi out and to optimally manage that traffic such that you know exactly what beam it's going to and worth Antenna is coming from that turns out to be a non trivial, you know, partial differential equation. So next thing you know, you've got HPC in there as and he didn't expect it because there's so much data to be sent, you really have to do some data reduction and data processing almost at the point of inception, if not at the point of aggregation. So that has led to edge computing and edge data centers. And that, too, is now. People want some level of computational capability at that place like you're building a microcontroller, which traditionally would just be a, you know, small, low power, low cost thing. And people want victor instructions. There. People want matrix algebra there because it makes sense to process the data before you have to ship it. So HPCs cropping up really everywhere. And then finally, when you're trying to accelerate things that obviously GP use have been a great example of that mixed signal technologies air coming to do analog and digital at the same time, quantum technologies coming so you could do the you know, the usual analysts to buy to where you have analog, digital, classical quantum and then see which, you know, with what lies where all of that is coming. And all of that is essentially resting on HBC. >>That's interesting. I didn't realize that HBC had that position in five G with multi and multi out. That's great example and then I o t. I want to ask you about that because there's a lot of discussion about real time influencing AI influencing at the edge on you're seeing sort of new computing architectures, potentially emerging, uh, video. The acquisition of arm Perhaps, you know, amore efficient way, maybe a lower cost way of doing specialized computing at the edge it, But it sounds like you're envisioning, actually, supercomputing at the edge. Of course, we've talked to Dr Mark Fernandez about space born computers. That's like the ultimate edge you got. You have supercomputers hanging on the ceiling of the International space station, but But how far away are we from this sort of edge? Maybe not. Space is an extreme example, but you think factories and windmills and all kinds of edge examples where supercomputing is is playing a local role. >>Well, I think initially you're going to see it on base stations, Antenna towers, where you're aggregating data from a large number of endpoints and sensors that are gathering the data, maybe do some level of local processing and then ship it to the local antenna because it's no more than 100 m away sort of a thing. But there is enough there that that thing can now do the processing and do some level of learning and decide what data to ship back to the cloud and what data to get rid of and what data to just hold. Or now those edge data centers sitting on top of an antenna. They could have a half a dozen GPS in them. They're pretty powerful things. They could have, you know, one they could have to, but but it could be depending on what you do. A good a good case study. There is like surveillance cameras. You don't really need to ship every image back to the cloud. And if you ever need it, the guy who needs it is gonna be on the scene, not back at the cloud. So there is really no sense in sending it, Not certainly not every frame. So maybe you can do some processing and send an image every five seconds or every 10 seconds, and that way you can have a record of it. But you've reduced your bandwidth by orders of magnitude. So things like that are happening. And toe make sense of all of that is to recognize when things changed. Did somebody come into the scene or is it just you know that you know, they became night, So that's sort of a decision. Cannot be automated and fundamentally what is making it happen? It may not be supercomputing exa scale class, but it's definitely HPCs, definitely numerically oriented technologies. >>Shane, what do you see happening in chip architectures? Because, you see, you know the classical intel they're trying to put as much function on the real estate as possible. We've seen the emergence of alternative processors, particularly, uh, GP use. But even if f b g A s, I mentioned the arm acquisition, so you're seeing these alternative processors really gain momentum and you're seeing data processing units emerge and kind of interesting trends going on there. What do you see? And what's the relationship to HPC? >>Well, I think a few things are going on there. Of course, one is, uh, essentially the end of Moore's law, where you cannot make the cycle time be any faster, so you have to do architectural adjustments. And then if you have a killer app that lends itself to large volume, you can build silicon. That is especially good for that now. Graphics and gaming was an example of that, and people said, Oh my God, I've got all these cores in there. Why can't I use it for computation? So everybody got busy making it 64 bit capable and some grass capability, And then people say, Oh, I know I can use that for a I And you know, now you move it to a I say, Well, I don't really need 64 but maybe I can do it in 32 or 16. So now you do it for that, and then tens, of course, come about. And so there's that sort of a progression of architecture, er trumping, basically cycle time. That's one thing. The second thing is scale out and decentralization and distributed computing. And that means that the inter communication and intra communication among all these notes now becomes an issue big enough issue that maybe it makes sense to go to a DPU. Maybe it makes sense to go do some level of, you know, edge data centers like we were talking about on then. The third thing, really is that in many of these cases you have data streaming. What is really coming from I o t, especially an edge, is that data is streaming and when data streaming suddenly new architectures like F B G. A s become really interesting and and and hold promise. So I do see, I do see FPG's becoming more prominent just for that reason, but then finally got a program all of these things on. That's really a difficulty, because what happens now is that you need to get three different ecosystems together mobile programming, embedded programming and cloud programming. And those are really three different developer types. You can't hire somebody who's good at all three. I mean, maybe you can, but not many. So all of that is challenges that are driving this this this this industry, >>you kind of referred to this distributed network and a lot of people you know, they refer to this. The next generation cloud is this hyper distributed system. When you include the edge and multiple clouds that etcetera space, maybe that's too extreme. But to your point, at least I inferred there's a There's an issue of Leighton. See, there's the speed of light s So what? What? What is the implication then for HBC? Does that mean I have tow Have all the data in one place? Can I move the compute to the data architecturally, What are you seeing there? >>Well, you fundamentally want to optimize when to move data and when to move, Compute. Right. So is it better to move data to compute? Or is it better to bring compute to data and under what conditions? And the dancer is gonna be different for different use cases. It's like, really, is it worth my while to make the trip, get my processing done and then come back? Or should I just developed processing capability right here? Moving data is really expensive and relatively speaking. It has become even more expensive, while the price of everything has dropped down its price has dropped less than than than like processing. So it is now starting to make sense to do a lot of local processing because processing is cheap and moving data is expensive Deep Use an example of that, Uh, you know, we call this in C two processing like, you know, let's not move data. If you don't have to accept that we live in the age of big data, so data is huge and wants to be moved. And that optimization, I think, is part of what you're what you're referring to. >>Yeah, So a couple examples might be autonomous vehicles. You gotta have to make decisions in real time. You can't send data back to the cloud flip side of that is we talk about space borne computers. You're collecting all this data You can at some point. You know, maybe it's a year or two after the lived out its purpose. You ship that data back and a bunch of disk drives or flash drives, and then load it up into some kind of HPC system and then have at it and then you doom or modeling and learn from that data corpus, right? I mean those air, >>right? Exactly. Exactly. Yeah. I mean, you know, driverless vehicles is a great example, because it is obviously coming fast and furious, no pun intended. And also, it dovetails nicely with the smart city, which dovetails nicely with I o. T. Because it is in an urban area. Mostly, you can afford to have a lot of antenna, so you can give it the five g density that you want. And it requires the Layton sees. There's a notion of how about if my fleet could communicate with each other. What if the car in front of me could let me know what it sees, That sort of a thing. So, you know, vehicle fleets is going to be in a non opportunity. All of that can bring all of what we talked about. 21 place. >>Well, that's interesting. Okay, so yeah, the fleets talking to each other. So kind of a Byzantine fault. Tolerance. That problem that you talk about that z kind of cool. I wanna I wanna sort of clothes on quantum. It's hard to get your head around. Sometimes You see the demonstrations of quantum. It's not a one or zero. It could be both. And you go, What? How did come that being so? And And of course, there it's not stable. Uh, looks like it's quite a ways off, but the potential is enormous. It's of course, it's scary because we think all of our, you know, passwords are already, you know, not secure. And every password we know it's gonna get broken. But give us the give us the quantum 101 And let's talk about what the implications. >>All right, very well. So first off, we don't need to worry about our passwords quite yet. That that that's that's still ways off. It is true that analgesic DM came up that showed how quantum computers can fact arise numbers relatively fast and prime factory ization is at the core of a lot of cryptology algorithms. So if you can fact arise, you know, if you get you know, number 21 you say, Well, that's three times seven, and those three, you know, three and seven or prime numbers. Uh, that's an example of a problem that has been solved with quantum computing, but if you have an actual number, would like, you know, 2000 digits in it. That's really harder to do. It's impossible to do for existing computers and even for quantum computers. Ways off, however. So as you mentioned, cubits can be somewhere between zero and one, and you're trying to create cubits Now there are many different ways of building cubits. You can do trapped ions, trapped ion trapped atoms, photons, uh, sometimes with super cool, sometimes not super cool. But fundamentally, you're trying to get these quantum level elements or particles into a superimposed entanglement state. And there are different ways of doing that, which is why quantum computers out there are pursuing a lot of different ways. The whole somebody said it's really nice that quantum computing is simultaneously overhyped and underestimated on. And that is that is true because there's a lot of effort that is like ways off. On the other hand, it is so exciting that you don't want to miss out if it's going to get somewhere. So it is rapidly progressing, and it has now morphed into three different segments. Quantum computing, quantum communication and quantum sensing. Quantum sensing is when you can measure really precise my new things because when you perturb them the quantum effects can allow you to measure them. Quantum communication is working its way, especially in financial services, initially with quantum key distribution, where the key to your cryptography is sent in a quantum way. And the data sent a traditional way that our efforts to do quantum Internet, where you actually have a quantum photon going down the fiber optic lines and Brookhaven National Labs just now demonstrated a couple of weeks ago going pretty much across the, you know, Long Island and, like 87 miles or something. So it's really coming, and and fundamentally, it's going to be brand new algorithms. >>So these examples that you're giving these air all in the lab right there lab projects are actually >>some of them are in the lab projects. Some of them are out there. Of course, even traditional WiFi has benefited from quantum computing or quantum analysis and, you know, algorithms. But some of them are really like quantum key distribution. If you're a bank in New York City, you very well could go to a company and by quantum key distribution services and ship it across the you know, the waters to New Jersey on that is happening right now. Some researchers in China and Austria showed a quantum connection from, like somewhere in China, to Vienna, even as far away as that. When you then put the satellite and the nano satellites and you know, the bent pipe networks that are being talked about out there, that brings another flavor to it. So, yes, some of it is like real. Some of it is still kind of in the last. >>How about I said I would end the quantum? I just e wanna ask you mentioned earlier that sort of the geopolitical battles that are going on, who's who are the ones to watch in the Who? The horses on the track, obviously United States, China, Japan. Still pretty prominent. How is that shaping up in your >>view? Well, without a doubt, it's the US is to lose because it's got the density and the breadth and depth of all the technologies across the board. On the other hand, information age is a new eyes. Their revolution information revolution is is not trivial. And when revolutions happen, unpredictable things happen, so you gotta get it right and and one of the things that these technologies enforce one of these. These revolutions enforce is not just kind of technological and social and governance, but also culture, right? The example I give is that if you're a farmer, it takes you maybe a couple of seasons before you realize that you better get up at the crack of dawn and you better do it in this particular season. You're gonna starve six months later. So you do that to three years in a row. A culture has now been enforced on you because that's how it needs. And then when you go to industrialization, you realize that Gosh, I need these factories. And then, you know I need workers. And then next thing you know, you got 9 to 5 jobs and you didn't have that before. You don't have a command and control system. You had it in military, but not in business. And and some of those cultural shifts take place on and change. So I think the winner is going to be whoever shows the most agility in terms off cultural norms and governance and and and pursuit of actual knowledge and not being distracted by what you think. But what actually happens and Gosh, I think these exa scale technologies can make the difference. >>Shaheen Khan. Great cast. Thank you so much for joining us to celebrate the extra scale day, which is, uh, on 10. 18 on dso. Really? Appreciate your insights. >>Likewise. Thank you so much. >>All right. Thank you for watching. Keep it right there. We'll be back with our next guest right here in the Cube. We're celebrating Exa scale day right back.