>>How do y'all, and welcome back to Supercomputing 2022. We're the Cube, and we are live from Dallas, Texas. I'm joined by my co-host, David Nicholson. David, hello. Hello. We are gonna be talking about data and enterprise AI at scale during this segment. And we have the pleasure of being joined by both Dell and Navidia. Anthony and Bob, welcome to the show. How you both doing? Doing good. >>Great. Great show so far. >>Love that. Enthusiasm, especially in the afternoon on day two. I think we all, what, what's in that cup? Is there something exciting in there that maybe we should all be sharing with you? >>Just say it's just still Yeah, water. >>Yeah. Yeah. I love that. So I wanna make sure that, cause we haven't talked about this at all during the show yet, on the cube, I wanna make sure that everyone's on the same page when we're talking about data unstructured versus structured data. I, it's in your title, Anthony, tell me what, what's the difference? >>Well, look, the world has been based in analytics around rows and columns, spreadsheets, data warehouses, and we've made predictions around the forecast of sales maintenance issues. But when we take computers and we give them eyes, ears, and fingers, cameras, microphones, and temperature and vibration sensors, we now translate that into more human experience. But that kind of data, the sensor data, that video camera is unstructured or semi-structured, that's what that >>Means. We live in a world of unstructured data structure is something we add to later after the fact. But the world that we see and the world that we experience is unstructured data. And one of the promises of AI is to be able to take advantage of everything that's going on around us and augment that, improve that, solve problems based on that. And so if we're gonna do that job effectively, we can't just depend on structured data to get the problem done. We have to be able to incorporate everything that we can see here, taste, smell, touch, and use >>That as, >>As part of the problem >>Solving. We want the chaos, bring it. >>Chaos has been a little bit of a theme of our >>Show. It has been, yeah. And chaos is in the eye of the beholder. You, you think about, you think about the reason for structuring data to a degree. We had limited processing horsepower back when everything was being structured as a way to allow us to be able to, to to reason over it and gain insights. So it made sense to put things into rows and tables. How does, I'm curious, diving right into where Nvidia fits into this, into this puzzle, how does NVIDIA accelerate or enhance our ability to glean insight from or reason over unstructured data in particular? >>Yeah, great question. It's really all about, I would say it's all about ai and Invidia is a leader in the AI space. We've been investing and focusing on AI since at least 2012, if not before, accelerated computing that we do it. Invidia is an important part of it, really. We believe that AI is gonna revolutionize nearly every aspect of computing. Really nearly every aspect of problem solving, even nearly every aspect of programming. And one of the reasons is for what we're talking about now is it's a little impact. Being able to incorporate unstructured data into problem solving is really critical to being able to solve the next generation of problems. AI unlocks, tools and methodologies that we can realistically do that with. It's not realistic to write procedural code that's gonna look at a picture and solve all the problems that we need to solve if we're talking about a complex problem like autonomous driving. But with AI and its ability to naturally absorb unstructured data and make intelligent reason decisions based on it, it's really a breakthrough. And that's what NVIDIA's been focusing on for at least a decade or more. >>And how does NVIDIA fit into Dell's strategy? >>Well, I mean, look, we've been partners for many, many years delivering beautiful experiences on workstations and laptops. But as we see the transition away from taking something that was designed to make something pretty on screen to being useful in solving problems in life sciences, manufacturing in other places, we work together to provide integrated solutions. So take for example, the dgx a 100 platform, brilliant design, revolutionary bus technologies, but the rocket ship can't go to Mars without the fuel. And so you need a tank that can scale in performance at the same rate as you throw GPUs at it. And so that's where the relationship really comes alive. We enable people to curate the data, organize it, and then feed those algorithms that get the answers that Bob's been talking about. >>So, so as a gamer, I must say you're a little shot at making things pretty on a screen. Come on. That was a low blow. That >>Was a low blow >>Sassy. What I, >>I Now what's in your cup? That's what I wanna know, Dave, >>I apparently have the most boring cup of anyone on you today. I don't know what happened. We're gonna have to talk to the production team. I'm looking at all of you. We're gonna have to make that better. One of the themes that's been on this show, and I love that you all embrace the chaos, we're, we're seeing a lot of trend in the experimentation phase or stage rather. And it's, we're in an academic zone of it with ai, companies are excited to adopt, but most companies haven't really rolled out their strategy. What is necessary for us to move from this kind of science experiment, science fiction in our heads to practical application at scale? Well, >>Let me take this, Bob. So I've noticed there's a pattern of three levels of maturity. The first level is just what you described. It's about having an experience, proof of value, getting stakeholders on board, and then just picking out what technology, what algorithm do I need? What's my data source? That's all fun, but it is chaos over time. People start actually making decisions based on it. This moves us into production. And what's important there is normality, predictability, commonality across, but hidden and embedded in that is a center of excellence. The community of data scientists and business intelligence professionals sharing a common platform in the last stage, we get hungry to replicate those results to other use cases, throwing even more information at it to get better accuracy and precision. But to do this in a budget you can afford. And so how do you figure out all the knobs and dials to turn in order to make, take billions of parameters and process that, that's where casual, what's >>That casual decision matrix there with billions of parameters? >>Yeah. Oh, I mean, >>But you're right that >>That's, that's exactly what we're, we're on this continuum, and this is where I think the partnership does really well, is to marry high performant enterprise grade scalability that provides the consistency, the audit trail, all of the things you need to make sure you don't get in trouble, plus all of the horsepower to get to the results. Bob, what would you >>Add there? I think the thing that we've been talking about here is complexity. And there's complexity in the AI problem solving space. There's complexity everywhere you look. And we talked about the idea that NVIDIA can help with some of that complexity from the architecture and the software development side of it. And Dell helps with that in a whole range of ways, not the least of which is the infrastructure and the server design and everything that goes into unlocking the performance of the technology that we have available to us today. So even the center of excellence is an example of how do I take this incredibly complex problem and simplify it down so that the real world can absorb and use this? And that's really what Dell and Vidia are partnering together to do. And that's really what the center of excellence is. It's an idea to help us say, let's take this extremely complex problem and extract some good value out of >>It. So what is Invidia's superpower in this realm? I mean, look, we're we are in, we, we are in the era of Yeah, yeah, yeah. We're, we're in a season of microprocessor manufacturers, one uping, one another with their latest announcements. There's been an ebb and a flow in our industry between doing everything via the CPU versus offloading processes. Invidia comes up and says, Hey, hold on a second, gpu, which again, was focused on graphics processing originally doing something very, very specific. How does that translate today? What's the Nvidia again? What's, what's, what's the superpower? Because people will say, well, hey, I've got a, I've got a cpu, why do I need you? >>I think our superpower is accelerated computing, and that's really a hardware and software thing. I think your question is slanted towards the hardware side, which is, yes, it is very typical and we do make great processors, but the processor, the graphics processor that you talked about from 10 or 20 years ago was designed to solve a very complex task. And it was exquisitely designed to solve that task with the resources that we had available at that time. Time. Now, fast forward 10 or 15 years, we're talking about a new class of problems called ai. And it requires both exquisite, soft, exquisite processor design as well as very complex and exquisite software design sitting on top of it as well. And the systems and infrastructure knowledge, high performance storage and everything that we're talking about in the solution today. So Nvidia superpower is really about that accelerated computing stack at the bottom. You've got hardware above that, you've got systems above that, you have middleware and libraries and above that you have what we call application SDKs that enable the simplification of this really complex problem to this domain or that domain or that domain, while still allowing you to take advantage of that processing horsepower that we put in that exquisitely designed thing called the gpu >>Decreasing complexity and increasing speed to very key themes of the show. Shocking, no one, you all wanna do more faster. Speaking of that, and I'm curious because you both serve a lot of different unique customers, verticals and use cases, is there a specific project that you're allowed to talk about? Or, I mean, you know, you wanna give us the scoop, that's totally cool too. We're here for the scoop on the cube, but is there a specific project or use case that has you personally excited Anthony? We'll start with that. >>Look, I'm, I've always been a big fan of natural language processing. I don't know why, but to derive intent based on the word choices is very interesting to me. I think what compliments that is natural language generation. So now we're having AI programs actually discover and describe what's inside of a package. It wouldn't surprise me that over time we move from doing the typical summary on the economic, the economics of the day or what happened in football. And we start moving that towards more of the creative advertising and marketing arts where you are no longer needed because the AI is gonna spit out the result. I don't think we're gonna get there, but I really love this idea of human language and computational linguistics. >>What a, what a marriage. I agree. Think it's fascinating. What about you, Bob? It's got you >>Pumped. The thing that really excites me is the problem solving, sort of the tip of the spear in problem solving. The stuff that you've never seen before, the stuff that you know, in a geeky way kind of takes your breath away. And I'm gonna jump or pivot off of what Anthony said. Large language models are really one of those areas that are just, I think they're amazing and they're just kind of surprising everyone with what they can do here on the show floor. I was looking at a demonstration from a large language model startup, basically, and they were showing that you could ask a question about some obscure news piece that was reported only in a German newspaper. It was about a little shipwreck that happened in a hardware. And I could type in a query to this system and it would immediately know where to find that information as if it read the article, summarized it for you, and it even could answer questions that you could only only answer by looking pic, looking at pictures in that article. Just amazing stuff that's going on. Just phenomenal >>Stuff. That's a huge accessibility. >>That's right. And I geek out when I see stuff like that. And that's where I feel like all this work that Dell and Invidia and many others are putting into this space is really starting to show potential in ways that we wouldn't have dreamed of really five years ago. Just really amazing. And >>We see this in media and entertainment. So in broadcasting, you have a sudden event, someone leaves this planet where they discover something new where they get a divorce and they're a major quarterback. You wanna go back somewhere in all of your archives to find that footage. That's a very laborist project. But if you can use AI technology to categorize that and provide the metadata tag so you can, it's searchable, then we're off to better productions, more interesting content and a much richer viewer experience >>And a much more dynamic picture of what's really going on. Factoring all of that in, I love that. I mean, David and I are both nerds and I know we've had take our breath away moments, so I appreciate that you just brought that up. Don't worry, you're in good company. In terms of the Geek Squad over >>Here, I think actually maybe this entire show for Yes, exactly. >>I mean, we were talking about how steampunk some of the liquid cooling stuff is, and you know, this is the only place on earth really, or the only show where you would come and see it at this level in scale and, and just, yeah, it's, it's, it's very, it's very exciting. How important for the future of innovation in HPC are partnerships like the one that Navia and Dell have? >>You wanna start? >>Sure, I would, I would just, I mean, I'm gonna be bold and brash and arrogant and say they're essential. Yeah, you don't not, you do not want to try and roll this on your own. This is, even if we just zoomed in to one little beat, little piece of the technology, the software stack that do modern, accelerated deep learning is incredibly complicated. There can be easily 20 or 30 components that all have to be the right version with the right buttons pushed, built the right way, assembled the right way, and we've got lots of technologies to help with that. But you do not want to be trying to pull that off on your own. That's just one little piece of the complexity that we talked about. And we really need, as technology providers in this space, we really need to do as much as we do to try to unlock the potential. We have to do a lot to make it usable and capable as well. >>I got a question for Anthony. All >>Right, >>So in your role, and I, and I'm, I'm sort of, I'm sort of projecting here, but I think, I think, I think your superpower personally is likely in the realm of being able to connect the dots between technology and the value that that technology holds in a variety of contexts. That's right. Whether it's business or, or whatever, say sentences. Okay. Now it's critical to have people like you to connect those dots. Today in the era of pervasive ai, how important will it be to have AI have to explain its answer? In other words, words, should I trust the information the AI is giving me? If I am a decision maker, should I just trust it on face value? Or am I going to want a demand of the AI kind of what you deliver today, which is No, no, no, no, no, no. You need to explain this to me. How did you arrive at that conclusion, right? How important will that be for people to move forward and trust the results? We can all say, oh hey, just trust us. Hey, it's ai, it's great, it's got Invidia, you know, Invidia acceleration and it's Dell. You can trust us, but come on. So many variables in the background. It's >>An interesting one. And explainability is a big function of ai. People want to know how the black box works, right? Because I don't know if you have an AI engine that's looking for potential maladies in an X-ray, but it misses it. Do you sue the hospital, the doctor or the software company, right? And so that accountability element is huge. I think as we progress and we trust it to be part of our everyday decision making, it's as simply as a recommendation engine. It isn't actually doing all of the decisions. It's supporting us. We still have, after decades of advanced technology algorithms that have been proven, we can't predict what the market price of any object is gonna be tomorrow. And you know why? You know why human beings, we are so unpredictable. How we feel in the moment is radically different. And whereas we can extrapolate for a population to an individual choice, we can't do that. So humans and computers will not be separated. It's a, it's a joint partnership. But I wanna get back to your point, and I think this is very fundamental to the philosophy of both companies. Yeah, it's about a community. It's always about the people sharing ideas, getting the best. And anytime you have a center of excellence and algorithm that works for sales forecasting may actually be really interesting for churn analysis to make sure the employees or students don't leave the institution. So it's that community of interest that I think is unparalleled at other conferences. This is the place where a lot of that happens. >>I totally agree with that. We felt that on the show. I think that's a beautiful note to close on. Anthony, Bob, thank you so much for being here. I'm sure everyone feels more educated and perhaps more at peace with the chaos. David, thanks for sitting next to me asking the best questions of any host on the cube. And thank you all for being a part of our community. Speaking of community here on the cube, we're alive from Dallas, Texas. It's super computing all week. My name is Savannah Peterson and I'm grateful you're here. >>So I.

>>Good afternoon everyone, and thank you so much for joining us. My name is Savannah Peterson, joined by my co-host Paul for the afternoon. Very excited. Oh, Savannah. Hello. I'm, I'm pumped for this. This is our first bit together. Exactly. >>It's gonna be fun. Yes. We have a great guest to kick off with. >>We absolutely do. We're at Supercomputing 2022 today, and very excited to talk to our next guest. We're gonna be talking about data at scale and data that really matters to us joining us. Kelly Gayer, thank you so much for being here and you are with tech. Tell everyone what TAC is. >>Tech is the Texas Advanced Computing Center at the University of Texas at Austin. And thank you so much for having me here. >>It is wonderful to have you. Your smile's contagious. And one of the themes that's come up a lot with all of our guests, and we just talked about it, is how good it is to be back in person, how good it is to be around our hardware, community tech. You did some very interesting research during the pandemic. Can you tell us about that? >>I can. I did. So when we realized sort of mid-March, we realized that, that this was really not normal times and the pandemic was statement. Yes. That pandemic was really gonna touch everyone. I think a lot of us at the center and me personally, we dropped everything to plug in and that's what we do. So UT's tagline is what starts here changes the world and tax tagline is powering discoveries that change the world. So we're all about impact, but I plugged in with the research group there at UT Austin, Dr. Lauren Myers, who's an epidemiologist, and just we figured out how to plug in and compute so that we could predict the spread of, of Covid 19. >>And you did that through the use of mobility data, cell phone signals. Tell us more about what exactly you were choreographing. >>Yeah, so that was really interesting. Safe graph during the pandemic made their mobility data. Typically it was used for marketing purposes to know who was going into Walmart. The offenses >>For advertising. >>Absolutely, yeah. They made all of their mobility data available for free to people who were doing research and plugging in trying to understand Covid. 19, I picked that data up and we used it as a proxy for human behavior. So we knew we had some idea, we got weekly mobility updates, but it was really mobility all day long, you know, anonymized. I didn't know who they were by cell phones across the US by census block group or zip code if we wanted to look at it that way. And we could see how people were moving around. We knew what their neighbor, their home neighborhoods were. We knew how they were traveling or not traveling. We knew where people were congregating, and we could get some idea of, of how people were behaving. Were they really, were they really locking down or were they moving in their neighborhoods or were they going outside of their neighborhoods? >>What a, what a fascinating window into our pandemic lives. So now that you were able to do this for this pandemic, as we look forward, what have you learned? How quickly could we forecast? What's the prognosis? >>Yeah, so we, we learned a tremendous amount. I think during the pandemic we were reacting, we were really trying. It was a, it was an interesting time as a scientist, we were reacting to things almost as if the earth was moving underneath us every single day. So it was something new every day. And I've told people since I've, I haven't, I haven't worked that hard since I was a graduate student. So it was really daylight to dark 24 7 for a long period of time because it was so important. And we knew, we, we knew we were, we were being a part of history and affecting something that was gonna make a difference for a really long time. And, and I think what we've learned is that indeed there is a lot of data being collected that we can use for good. We can really understand if we get organized and we get set up, we can use this data as a means of perhaps predicting our next pandemic or our next outbreak of whatever. It is almost like using it as a canary in the coal mine. There's a lot in human behavior we can use, given >>All the politicization of, of this last pandemic, knowing what we know now, making us better prepared in theory for the next one. How confident are you that at least in the US we will respond proactively and, and effectively when the next one comes around? >>Yeah, I mean, that's a, that's a great question and, and I certainly understand why you ask. I think in my experience as a scientist, certainly at tech, the more transparent you are with what you do and the more you explain things. Again, during the pandemic, things were shifting so rapidly we were reacting and doing the best that we could. And I think one thing we did right was we admitted where we felt uncertain. And that's important. You have to really be transparent to the general public. I, I don't know how well people are gonna react. I think if we have time to prepare, to communicate and always be really transparent about it. I think those are three factors that go into really increasing people's trust. >>I think you nailed it. And, and especially during times of chaos and disaster, you don't know who to trust or what to believe. And it sounds like, you know, providing a transparent source of truth is, is so critical. How do you protect the sensitive data that you're working with? I know it's a top priority for you and the team. >>It is, it is. And we, we've adopted the medical mantra, do no harm. So we have, we feel a great responsibility there. There's, you know, two things that you have to really keep in mind when you've got sensitive data. One is the physical protection of it. And so that's, that's governed by rule, federal rules, hipaa, ferpa, whatever, whatever kind of data that you have. So we certainly focus on the physical protection of it, but there's also sort of the ethical protection of it. What, what is the quote? There's lies, damn lies and statistics. >>Yes. Twain. >>Yeah. So you, you really have to be responsible with what you're doing with the data, how you're portraying the results. And again, I think it comes back to transparency is is basically if people are gonna reproduce what I did, I have to be really transparent with what I did. >>I, yeah, I think that's super important. And one of the themes with, with HPC that we've been talking about a lot too is, you know, do people trust ai? Do they trust all the data that's going into these systems? And I love that you just talked about the storytelling aspect of that, because there is a duty, it's not, you can cut data kind of however you want. I mean, I come from marketing background and we can massage it to, to do whatever we want. So in addition to being the deputy director at Tech, you are also the DEI officer. And diversity I know is important to you probably both as an individual, but also in the work that you're doing. Talk to us about that. >>Yeah, I mean, I, I very passionate about diversity, equity and inclusion in a sense of belongingness. I think that's one of the key aspects of it. Core >>Of community too. >>I got a computer science degree back in the eighties. I was akin to a unicorn in a, in an engineering computer science department. And, but I was really lucky in a couple of respects. I had a, I had a father that was into science that told me I could do anything I, I wanted to set my mind to do. So that was my whole life, was really having that support system. >>He was cheers to dad. >>Yeah. Oh yeah. And my mom as well, actually, you know, they were educators. I grew up, you know, in that respect, very, very privileged, but it was still really hard to make it. And I couldn't have told you back in that time why I made it and, and others didn't, why they dropped out. But I made it a mission probably back, gosh, maybe 10, 15 years ago, that I was really gonna do all that I could to change the needle. And it turns out that there are a number of things that you can do grassroots. There are certainly best practices. There are rules and there are things that you really, you know, best practices to follow to make people feel more included in an organization, to feel like they belong it, shared mission. But there are also clever things that you can do with programming to really engage students, to meet people and students where they are interested and where they are engaged. And I think that's what, that's what we've done over, you know, the course of our programming over the course of about maybe since 2016. We have built a lot of programming ATAC that really focuses on that as well, because I'm determined the needle is gonna change before it's all said and done. It just really has to. >>So what, what progress have you made and what goals have you set in this area? >>Yeah, that, that's a great question. So, you know, at first I was a little bit reluctant to set concrete goals because I really didn't know what we could accomplish. I really wasn't sure what grassroots efforts was gonna be able to, you're >>So honest, you can tell how transparent you are with the data as well. That's >>Great. Yeah, I mean, if I really, most of the successful work that I've done is both a scientist and in the education and outreach space is really trust relationships. If I break that trust, I'm done. I'm no longer effective. So yeah, I am really transparent about it. But, but what we did was, you know, the first thing we did was we counted, you know, to the extent that we could, what does the current picture look like? Let's be honest about it. Start where we are. Yep. It was not a pretty picture. I mean, we knew that anecdotally it was not gonna be a great picture, but we put it out there and we leaned into it. We said, this is what it is. We, you know, I hesitated to say we're gonna look 10% better next year because I'm, I'm gonna be honest, I don't always know we're gonna do our best. >>The things that I think we did really well was that we stopped to take time to talk and find out what people were interested in. It's almost like being present and listening. My grandmother had a saying, you have two errors in one mouth for a reason, just respect the ratio. Oh, I love that. Yeah. And I think it's just been building relationships, building trust, really focusing on making a difference, making it a priority. And I think now what we're doing is we've been successful in pockets of people in the center and we are, we are getting everybody on board. There's, there's something everyone can do, >>But the problem you're addressing doesn't begin in college. It begins much, much, that's right. And there's been a lot of talk about STEM education, particularly for girls, how they're pushed out of the system early on. Also for, for people of color. Do you see meaningful progress being made there now after years of, of lip service? >>I do. I do. But it is, again, grassroots. We do have a, a, a researcher who was a former teacher at the center, Carol Fletcher, who is doing research and for CS for all we know that the workforce, so if you work from the current workforce, her projected workforce backwards, we know that digital skills of some kind are gonna be needed. We also know we have a, a, a shortage. There's debate on how large that shortage is, but about roughly about 1 million unmet jobs was projected in 2020. It hasn't gotten a lot better. We can work that problem backwards. So what we do there is a little, like a scatter shot approach. We know that people come in all forms, all shapes, all sizes. They get interested for all different kinds of reasons. We expanded our set of pathways so that we can get them where they can get on to the path all the way back K through 12, that's Carol's work. Rosie Gomez at the center is doing sort of the undergraduate space. We've got Don Hunter that does it, middle school, high school space. So we are working all parts of the problem. I am pretty passionate about what we consider opportunity youth people who never had the opportunity to go to college. Is there a way that we can skill them and get, get them engaged in some aspect and perhaps get them into this workforce. >>I love that you're starting off so young. So give us an example of one of those programs. What are you talking to kindergartners about when it comes to CS education? >>You know, I mean, gaming. Yes. Right. It's what everybody can wrap their head around. So most kids have had some sort of gaming device. You talk in the context, in the context of something they understand. I'm not gonna talk to them about high performance computing. It, it would go right over their heads. And I think, yeah, you know, I, I'll go back to something that you said Paul, about, you know, girls were pushed out. I don't know that girls are being pushed out. I think girls aren't interested and things that are being presented and I think they, I >>Think you're generous. >>Yeah. I mean, I was a young girl and I don't know why I stayed. Well, I do know why I stayed with it because I had a father that saw something in me and I had people at critical points in my life that saw something in me that I didn't see. But I think if we ch, if we change the way we teach it, maybe in your words they don't get pushed out or they, or they won't lose interest. There's, there's some sort of computing in everything we do. Well, >>Absolutely. There's also the bro culture, which begins at a very early >>Age. Yeah, that's a different problem. Yeah. That's just having boys in the classroom. Absolutely. You got >>It. That's a whole nother case. >>That's a whole other thing. >>Last question for you, when we are sitting here, well actually I've got, it's two parter, let's put it that way. Is there a tool or something you wish you could flick a magic wand that would make your job easier? Where you, you know, is there, can you identify the, the linchpin in the DEI challenge? Or is it all still prototyping and iterating to figure out the best fit? >>Yeah, that is a, that's a wonderful question. I can tell you what I get frustrated with is that, that >>Counts >>Is that I, I feel like a lot of people don't fully understand the level of effort and engagement it takes to do something meaningful. The >>Commitment to a program, >>The commitment to a program. Totally agree. It's, there is no one and done. No. And in fact, if I do that, I will lose them forever. They'll be, they will, they will be lost in the space forever. Rather. The engagement is really sort of time intensive. It's relationship intensive, but there's a lot of follow up too. And the, the amount of funding that goes into this space really is not, it, it, it's not equal to the amount of time and effort that it really takes. And I think, you know, I think what you work in this space, you realize that what you gain is, is really more of, it's, it really feels good to make a difference in somebody's life, but it's really hard to do on a shoer budget. So if I could kind of wave a magic wand, yes, I would increase understanding. I would get people to understand that it's all of our responsibility. Yes, everybody is needed to make the difference and I would increase the funding that goes to the programs. >>I think that's awesome, Kelly, thank you for that. You all heard that. More funding for diversity, equity, and inclusion. Please Paul, thank you for a fantastic interview, Kelly. Hopefully everyone is now inspired to check out tac perhaps become a, a Longhorn, hook 'em and, and come deal with some of the most important data that we have going through our systems and predicting the future of our pandemics. Ladies and gentlemen, thank you for joining us online. We are here in Dallas, Texas at Supercomputing. My name is Savannah Peterson and I look forward to seeing you for our next segment.

(upbeat music) (logo swooshing) >> Good morning and welcome back to Dallas, ladies and gentlemen, we are here with theCUBE Live from Supercomputing 2022. David, my cohost, how are you doing? Exciting, day two, feeling good? >> Very exciting. Ready to start off the day. >> Very excited. We have two fascinating guests joining us to kick us off. Please welcome Pete and Armando. Gentlemen, thank you for being here with us. >> Thank you for having us. >> Thank you for having us. >> I'm excited that you're starting off the day because we've been hearing a lot of rumors about Ethernet as the fabric for HPC, but we really haven't done a deep dive yet during the show. You all seem all in on Ethernet. Tell us about that. Armando, why don't you start? >> Yeah, I mean, when you look at Ethernet, customers are asking for flexibility and choice. So when you look at HPC, InfiniBand's always been around, right? But when you look at where Ethernet's coming in, it's really our commercial in their enterprise customers. And not everybody wants to be in the top 500, what they want to do is improve their job time and improve their latency over the network. And when you look at Ethernet, you kind of look at the sweet spot between 8, 12, 16, 32 nodes, that's a perfect fit for Ethernet in that space and those types of jobs. >> I love that. Pete, you want to elaborate? >> Yeah, sure. I mean, I think one of the biggest things you find with Ethernet for HPC is that, if you look at where the different technologies have gone over time, you've had old technologies like, ATM, Sonic, Fifty, and pretty much everything is now kind of converged toward Ethernet. I mean, there's still some technologies such as InfiniBand, Omni-Path, that are out there. But basically, they're single source at this point. So what you see is that there is a huge ecosystem behind Ethernet. And you see that also the fact that Ethernet is used in the rest of the enterprise, is used in the cloud data centers, that is very easy to integrate HPC based systems into those systems. So as you move HPC out of academia into enterprise, into cloud service providers, it's much easier to integrate it with the same technology you're already using in those data centers, in those networks. >> So what's the state of the art for Ethernet right now? What's the leading edge? what's shipping now and what's in the near future? You're with Broadcom, you guys designed this stuff. >> Pete: Yeah. >> Savannah: Right. >> Yeah, so leading edge right now, got a couple things-- >> Savannah: We love good stage prop here on the theCUBE. >> Yeah, so this is Tomahawk 4. So this is what is in production, it's shipping in large data centers worldwide. We started sampling this in 2019, started going into data centers in 2020. And this is 25.6 terabytes per second. >> David: Okay. >> Which matches any other technology out there. Like if you look at say, InfinBand, highest they have right now that's just starting to get into production is 25.6 T. So state of the art right now is what we introduced, We announced this in August, This is Tomahawk 5, so this is 51.2 terabytes per second. So double the bandwidth, out of any other technology that's out there. And the important thing about networking technology is when you double the bandwidth, you don't just double the efficiency, actually, winds up being a factor of six efficiency. >> Savannah: Wow. >> 'Cause if you want, I can go into that, but... >> Why not? >> Well, what I want to know, please tell me that in your labs, you have a poster on the wall that says T five, with some like Terminator kind of character. (all laughs) 'Cause that would be cool. If it's not true, just don't say anything. I'll just... >> Pete: This can actually shift into a terminator. >> Well, so this is from a switching perspective. >> Yeah. >> When we talk about the end nodes, when we talk about creating a fabric, what's the latest in terms of, well, the nicks that are going in there, what speed are we talking about today? >> So as far as 30 speeds, it tends to be 50 gigabits per second. >> David: Okay. >> Moving to a hundred gig PAM-4. >> David: Okay. >> And we do see a lot of nicks in the 200 gig Ethernet port speed. So that would be four lanes, 50 gig. But we do see that advancing to 400 gig fairly soon, 800 gig in the future. But say state of the art right now, we're seeing for the end node tends to be 200 gig E based on 50 gig PAM-4. >> Wow. >> Yeah, that's crazy. >> Yeah, that is great. My mind is act actively blown. I want to circle back to something that you brought up a second ago, which I think is really astute. When you talked about HPC moving from academia into enterprise, you're both seeing this happen, where do you think we are on the adoption curve and sort of in that cycle? Armando, do you want to go? >> Yeah, well, if you look at the market research, they're actually telling you it's 50/50 now. So Ethernet is at the level of 50%, InfinBand's at 50%, right? >> Savannah: Interesting. >> Yeah, and so what's interesting to us, customers are coming to us and say, hey, we want to see flexibility and choice and, hey, let's look at Ethernet and let's look at InfiniBand. But what is interesting about this is that we're working with Broadcom, we have their chips in our lab, we their have switches in our lab. And really what we're trying to do is make it easy to simple and configure the network for essentially MPI. And so the goal here with our validated designs is really to simplify this. So if you have a customer that, hey, I've been InfiniBand but now I want to go Ethernet, there's going to be some learning curves there. And so what we want to do is really simplify that so that we can make it easy to install, get the cluster up and running and they can actually get some value out the cluster. >> Yeah, Pete, talk about that partnership. what does that look like? I mean, are you working with Dell before the T six comes out? Or you just say what would be cool is we'll put this in the T six? >> No, we've had a very long partnership both on the hardware and the software side. Dell's been an early adopter of our silicon. We've worked very closely on SI and Sonic on the operating system, and they provide very valuable feedback for us on our roadmap. So before we put out a new chip, and we have actually three different product lines within the switching group, within Broadcom, we've then gotten very valuable feedback on the hardware and on the APIs, on the operating system that goes on top of those chips. So that way when it comes to market, Dell can take it and deliver the exact features that they have in the current generation to their customers to have that continuity. And also they give us feedback on the next gen features they'd like to see again, in both the hardware and the software. >> So I'm fascinated by... I always like to know like what, yeah, exactly. Look, you start talking about the largest supercomputers, most powerful supercomputers that exist today, and you start looking at the specs and there might be two million CPUs, 2 million CPU cores. Exoflap of performance. What are the outward limits of T five in switches, building out a fabric, what does that look like? What are the increments in terms of how many... And I know it's a depends answer, but how many nodes can you support in a scale out cluster before you need another switch? Or what does that increment of scale look like today? >> Yeah, so this is 51.2 terabytes per second. Where we see the most common implementation based on this would be with 400 gig Ethernet ports. >> David: Okay. >> So that would be 128, 400 gig E ports connected to one chip. Now, if you went to 200 gig, which is kind of the state of the art for the nicks, you can have double that. So in a single hop, you can have 256 end nodes connected through one switch. >> Okay, so this T five, that thing right there, (all laughing) inside a sheet metal box, obviously you've got a bunch of ports coming out of that. So what's the form factor look like for where that T five sits? Is there just one in a chassis or you have.. What does that look like? >> It tends to be pizza boxes these days. What you've seen overall is that the industry's moved away from chassis for these high end systems more towardS pizza boxes. And you can have composable systems where, in the past you would have line cards, either the fabric cards that the line cards are plug into or interfaced to. These days what tends to happen is you'd have a pizza box and if you wanted to build up like a virtual chassis, what you would do is use one of those pizza boxes as the fabric card, one of them as the line card. >> David: Okay. >> So what we see, the most common form factor for this is they tend to be two, I'd say for North America, most common would be a 2RU, with 64 OSFP ports. And often each of those OSFP, which is an 800 gig E or 800 gig port, we've broken out into two 400 gig ports. >> So yeah, in 2RU, and this is all air cooled, in 2RU, you've got 51.2 T. We do see some cases where customers would like to have different optics and they'll actually deploy 4RU, just so that way they have the phase-space density. So they can plug in 128, say QSFP 112. But yeah, it really depends on which optics, if you want to have DAK connectivity combined with optics. But those are the two most common form factors. >> And Armando, Ethernet isn't necessarily Ethernet in the sense that many protocols can be run over it. >> Right. >> I think I have a projector at home that's actually using Ethernet physical connections. But, so what are we talking about here in terms of the actual protocol that's running over this? Is this exactly the same as what you think of as data center Ethernet, or is this RDMA over converged Ethernet? What Are we talking about? >> Yeah, so RDMA, right? So when you look at running, essentially HPC workloads, you have the NPI protocol, so message passing interface, right? And so what you need to do is you may need to make sure that that NPI message passing interface runs efficiently on Ethernet. And so this is why we want to test and validate all these different things to make sure that that protocol runs really, really fast on Ethernet. If you look at NPIs officially, built to, hey, it was designed to run on InfiniBand but now what you see with Broadcom, with the great work they're doing, now we can make that work on Ethernet and get same performance, so that's huge for customers. >> Both of you get to see a lot of different types of customers. I kind of feel like you're a little bit of a looking into the crystal ball type because you essentially get to see the future knowing what people are trying to achieve moving forward. Talk to us about the future of Ethernet in HPC in terms of AI and ML, where do you think we're going to be next year or 10 years from now? >> You want to go first or you want me to go first? >> I can start, yeah. >> Savannah: Pete feels ready. >> So I mean, what I see, I mean, Ethernet, what we've seen is that as far as on, starting off of the switch side, is that we've consistently doubled the bandwidth every 18 to 24 months. >> That's impressive. >> Pete: Yeah. >> Nicely done, casual, humble brag there. That was great, I love that. I'm here for you. >> I mean, I think that's one of the benefits of Ethernet, is the ecosystem, is the trajectory the roadmap we've had, I mean, you don't see that in any of the networking technology. >> David: More who? (all laughing) >> So I see that, that trajectory is going to continue as far as the switches doubling in bandwidth, I think that they're evolving protocols, especially again, as you're moving away from academia into the enterprise, into cloud data centers, you need to have a combination of protocols. So you'll probably focus still on RDMA, for the supercomputing, the AI/ML workloads. But we do see that as you have a mix of the applications running on these end nodes, maybe they're interfacing to the CPUs for some processing, you might use a different mix of protocols. So I'd say it's going to be doubling a bandwidth over time, evolution of the protocols. I mean, I expect that Rocky is probably going to evolve over time depending on the AI/ML and the HPC workloads. I think also there's a big change coming as far as the physical connectivity within the data center. Like one thing we've been focusing on is co-packed optics. So right now, this chip is, all the balls in the back here, there's electrical connections. >> How many are there, by the way? 9,000 plus on the back of that-- >> 9,352. >> I love how specific it is. It's brilliant. >> Yeah, so right now, all the SERDES, all the signals are coming out electrically based, but we've actually shown, we actually we have a version of Tomahawk 4 at 25.6 T that has co-packed optics. So instead of having electrical output, you actually have optics directly out of the package. And if you look at, we'll have a version of Tomahawk 5. >> Nice. >> Where it's actually even a smaller form factor than this, where instead of having the electrical output from the bottom, you actually have fibers that plug directly into the sides. >> Wow. Cool. >> So I see there's the bandwidth, there's radix's increasing, protocols, different physical connectivity. So I think there's a lot of things throughout, and the protocol stack's also evolving. So a lot of excitement, a lot of new technology coming to bear. >> Okay, You just threw a carrot down the rabbit hole. I'm only going to chase this one, okay? >> Peter: All right. >> So I think of individual discreet physical connections to the back of those balls. >> Yeah. >> So if there's 9,000, fill in the blank, that's how many connections there are. How do you do that many optical connections? What's the mapping there? What does that look like? >> So what we've announced for Tomahawk 5 is it would have FR4 optics coming out. So you'd actually have 512 fiber pairs coming out. So basically on all four sides, you'd have these fiber ribbons that come in and connect. There's actually fibers coming out of the sides there. We wind up having, actually, I think in this case, we would actually have 512 channels and it would wind up being on 128 actual fiber pairs because-- >> It's miraculous, essentially. >> Savannah: I know. >> Yeah. So a lot of people are going to be looking at this and thinking in terms of InfiniBand versus Ethernet, I think you've highlighted some of the benefits of specifically running Ethernet moving forward as HPC which sort of just trails slightly behind super computing as we define it, becomes more pervasive AI/ML. What are some of the other things that maybe people might not immediately think about when they think about the advantages of running Ethernet in that environment? Is it about connecting the HPC part of their business into the rest of it? What are the advantages? >> Yeah, I mean, that's a big thing. I think, and one of the biggest things that Ethernet has again, is that the data centers, the networks within enterprises, within clouds right now are run on Ethernet. So now, if you want to add services for your customers, the easiest thing for you to do is the drop in clusters that are connected with the same networking technology. So I think one of the biggest things there is that if you look at what's happening with some of the other proprietary technologies, I mean, in some cases they'll have two different types of networking technologies before they interface to Ethernet. So now you've got to train your technicians, you train your assist admins on two different network technologies. You need to have all the debug technology, all the interconnect for that. So here, the easiest thing is you can use Ethernet, it's going to give you the same performance and actually, in some cases, we've seen better performance than we've seen with Omni-Path, better than in InfiniBand. >> That's awesome. Armando, we didn't get to you, so I want to make sure we get your future hot take. Where do you see the future of Ethernet here in HPC? >> Well, Pete hit on a big thing is bandwidth, right? So when you look at, train a model, okay? So when you go and train a model in AI, you need to have a lot of data in order to train that model, right? So what you do is essentially, you build a model, you choose whatever neural network you want to utilize. But if you don't have a good data set that's trained over that model, you can't essentially train the model. So if you have bandwidth, you want big pipes because you have to move that data set from the storage to the CPU. And essentially, if you're going to do it maybe on CPU only, but if you do it on accelerators, well, guess what? You need a big pipe in order to get all that data through. And here's the deal, the bigger the pipe you have, the more data, the faster you can train that model. So the faster you can train that model, guess what? The faster you get to some new insight, maybe it's a new competitive advantage, maybe it's some new way you design a product, but that's a benefit of speed, you want faster, faster, faster. >> It's all about making it faster and easier-- for the users. >> Armando: It is. >> I love that. Last question for you, Pete, just because you've said Tomahawk seven times, and I'm thinking we're in Texas, stakes, there's a lot going on with that. >> Making me hungry. >> I know, exactly. I'm sitting out here thinking, man, I did not have big enough breakfast. How did you come up with the name Tomahawk? >> So Tomahawk, I think it just came from a list. So we have a tried end product line. >> Savannah: Ah, yes. >> Which is a missile product line. And Tomahawk is being kind of like the bigger and batter missile, so. >> Savannah: Love this. Yeah, I mean-- >> So do you like your engineers? You get to name it. >> Had to ask. >> It's collaborative. >> Okay. >> We want to make sure everyone's in sync with it. >> So just it's not the Aquaman tried. >> Right. >> It's the steak Tomahawk. I think we're good now. >> Now that we've cleared that-- >> Now we've cleared that up. >> Armando, Pete, it was really nice to have both you. Thank you for teaching us about the future of Ethernet and HCP. David Nicholson, always a pleasure to share the stage with you. And thank you all for tuning in to theCUBE live from Dallas. We're here talking all things HPC and supercomputing all day long. We hope you'll continue to tune in. My name's Savannah Peterson, thanks for joining us. (soft music)

>>How do y'all and welcome back to the cube where we're live from Dallas at a Supercomputing 2022. My name is Savannah Peterson. Joined with L AED today, as well as some very exciting guests talking about one of my favorite and most complex topics out there, talking about quantum a bit today. Please welcome Ken and Matthew. Thank you so much for reading here. Matthew. Everyone's gonna be able to see your shirt. What's going on with hybrid quantum? I have >>To ask. Wait, what is hybrid quantum? Yeah, let's not pretend that. >>Let's not >>Pretend that everybody knows, Everyone already knows what quantum computing is if we goes straight to highway. Yeah. Okay. So with the brief tour detour took qu regular quantum computing. Yeah, >>No, no. Yeah. Let's start with quantum start before. >>So you know, like regular computers made of transistors gives us ones and zeros, right? Binary, like you were talking about just like half of the Cheerios, right? The joke, it turns out there's some problems that even if we could build a computer as big as the whole universe, which would be pretty expensive, >>That might not be a bad thing, but >>Yeah. Yeah. Good for Dell Got mill. >>Yeah. >>Yeah. We wouldn't be able to solve them cuz they scale exponentially. And it turns out some of those problems have efficient solutions in quantum computing where we take any two state quantum system, which I'll explain in a sec and turn it into what we call a quantum bit or qubit. And those qubits can actually solve some problems that are just infeasible on even these world's largest computers by offering exponential advantage. And it turns out that today's quantum computers are a little too small and a little too noisy to do that alone. So by pairing a quantum computer with a classical computer, hence the partnership between IQ and Dell, you allow each kind of compute to do what it's best at and thereby get answers you can't get with either one alone. >>Okay. So the concept of introducing hybridity, I love that word bridge. I dunno if I made it up, but it's it for it. Let's about it. Abri, ding ding. So does this include simulating the quantum world within the, what was the opposite? The classical quantum world? Classical. Classical, classical computer. Yeah. So does it include the concept of simulating quantum in classical compute? >>Absolutely. >>Okay. How, how, how do, how do you do that? >>So there's simulators and emulators that effectively are programmed in exactly the same way that a physical quantum machine is through circuits translated into chasm or quantum assembly language. And those are the exact same ways that you would program either a physical q p or a simulated >>Q p. So, so access to quantum computing today is scarce, right? I mean it's, it's, it's, it's limited. So having the ability to have the world at large or a greater segment of society be able to access this through simulation is probably a good idea. >>Fair. It's absolutely a wonderful one. And so I often talk to customers and I tell them about the journey, which is hands on keyboard, learning, experimentation, building proof of concepts, and then finally productization. And you could do much of that first two steps anyway very robustly with simulation. >>It's much like classical computing where if you imagine back in the fifties, if, if the cube was at some conference in 1955, you know, we wouldn't have possibly been able to predict what we'd be doing with computing 70 years later, right? Yeah. That teenagers be making apps on their phones that changed the world, right? And so by democratizing access this way, suddenly we can open up all sorts of new use cases. We sort of like to joke, there's only a couple hundred people in the world who really know how to program quantum computers today. And so how are we gonna make thousands, tens of thousands, millions of quantum programmers? The answer is access and simulators are an amazingly accessible way for everyone to start playing around with the >>Fields. Very powerful tool. >>Wow. Yeah. I'm just thinking about how many, there's, are there really only hundreds of people who can program quantum computing? >>I kind of generally throw it out there and I say, you know, if you looked at a matrix of a thousand operations with hundreds of qubits, there's probably, I don't know, 2000 people worldwide that could program that type of a circuit. I mean it's a fairly complex circuit at that point and >>I, I mean it's pretty phenomenal When you think about how early we are in adoption and, and the rollout of this technology as a whole, can you see quite a bit as, as you look across your customer portfolio, what are some of the other trends you're seeing? >>Well, non quantum related trends or just any type you give us >>Both. >>Yeah. So >>We're a thought leader. This is >>Your moment. Yeah, so we do quite a bit. We see quite a bit actually. There's a lot of work happening at the edge as you're probably well aware of. And we see a lot of autonomous mobile robots. I actually lead the, the research office. So I get to see all the cool stuff that's really kind of emerging before it really regrets >>What's coming next. >>Let's see, Oh, I can't tell you what's coming next, but we see edge applications. Yes, we see a lot of, of AI applications and artificial intelligence is morphing dramatically through the number of frameworks and through the, the types and places you would place ai, even places I, I personally never thought we would go like manufacturing environments. Some places that were traditionally not very early adopters. We're seeing AI move very quickly in some of those areas. One of the areas that I'm really excited about is digital twins and the ability to eventually do, let's come up on acceleration with quantum technologies on, on things like computational fluid dynamics. And I think it's gonna be a wonderful, wonderful area for us moving forward. >>So, So I can hear the people screaming at the screen right now. Wait a minute, You said it was hybrid, you're only talking the front half. That's, that's cat. What about the back half? That's dog. What about the quantum part of it? So I, on Q and, and I apologize. Ion Q >>Ion >>Q, Yeah Ion Q cuz you never know. You never never know. Yeah. Where does the actual quantum come in? >>That's a great >>Question. So you guys have one of these things. >>Yeah, we've built, we currently have the world's best quantum computer by, by sub measures I drop there. Yeah, no big deal. Give me some snaps for that. Yeah, Ken knows how to pick em. Yeah, so right. Our, our approach, which is actually based on technology that's 50 years old, so it's quite, quite has a long history. The way we build atomic clocks is the basis for trapped eye quantum computing. And in fact the first quantum logic gate ever made in 1995 was at NIST where they modified their atomic clock experiment to do quantum gates. And that launched really the hardware experimentalist quantum Peter Revolution. And that was by Chris Monroe, our co-founder. So you know that history has flown directly into us. So to simplify, we start with an ion trap. Imagine a gold block with a bunch of electrodes that allow you to make precisely shaped electromagnetic fields, sort of like a rotating saddle. >>Then take a source of atoms. Now obviously we're all sources of atoms. We have a highly purified source of metal atium. We heat it up, we get a nice hot plume of atoms, we ionize those atoms with an ionizing later laser. Now they're hot and heavy and charged. So we can trap them in one of these fields. And now our electromagnetic field that's spitting rapidly holds the, the ions like balls in a bowl if you can imagine them. And they line up in a nice straight line and we hold them in place with these fields and with cooling laser beams. And up to now, that's how an atomic clock works. Trap an item and shine it with a laser beam. Count the oscillations, that's your clock. Now if you got 32 of those and you can manipulate their energy states, in our case we use the hyper fine energy states of the atom. >>But you can basically think of your high school chemistry where you have like an unexcited electron, an excited electron. Take your unexcited state as a zero, your excited state as a one. And it turns out with commercially available lasers, you can drive anywhere between a zero, a one or a super position of zero and one. And so that is our quantum bit, the hyper fine energy state of the atrium atom. And we just line up a bunch of them and through there access the magical powers of supervision entanglement, as we were talking about before, they don't really make sense to us here in the regular world, but >>They do exist. But what you just described is one cubit. That's right. And the way that you do it isn't exactly the same way that others who are doing quantum computing do it. That's right. Is that okay? >>And there's a lot of advantages to the trapped iron approach. So for example, you can also build a super conducting qubit where you, where you basically cool a chip to 47 mil kelvin and coerce millions of atoms to work together as a single system. The problem is that's not naturally quantum. So it's inherently noisy and it wants to deco here does not want to be a quantum bit. Whereas an atom is very happy to be by itself a qubit because we don't have to do anything to it. It's naturally quantum, if that makes sense. And so atomic qubits, like we use feature a few things. One the longest coherence times in the industry, meaning you can run very deep circuits, the most accurate operations, very low noise operations. And we don't have any wires. Our atoms are connected by laser light. That means you can connect any pair. So with some other technologies, the qubits are connected by wires. That means you can only run operations between physically connected qubits. It's like programming. If you could only use, for example, bits that are adjacent with an i untrapped approach, you can connect any pair so that all to all connectivity means your compilation is much more efficient and you can do much wider and deeper circuits. >>So what's the, what is the closest thing to a practical application that we've been able to achieve at this point? Question. And when I say practical, it doesn't have to be super practical. I mean, what is the, what is the sort of demonstration, the least esoteric demonstration of this at this point? >>To tie into what Ken was saying earlier, I think there's at least two areas that are very exciting. One is chemistry. Chemistry. So for example, you know, we have water in our cup and we understand water pretty well, but there's lots of molecules that in order to study them, we actually have to make them in a lab and do lots of experiments. And to give you a sense of the order of magnitude, if you wanted to understand the ground state of the caffeine molecule, which we all know and has 200 electrons, you would need to build a computer bigger than the moon. So, which is, you know, again, would be good profit for Dell, but probably not gonna happen time soon. That's >>Kind of fun to think about though. Yeah, that's a great analogy. That >>Was, yeah. And in fact it'd be like 10 moons of compute. Okay. So build 10 moons of >>Computer. I >>Love the sci-fi issue. Exactly. And now you can calculate caffeine, it's crazy or it just fits in a quantum computer the size of this table. And so we're using hybrid quantum computing now to start proving out these algorithms not for molecules as complex as caffeine or what we want in the future. Like biologics, you know, new cancer medications, new materials and so forth. But we are able to show, for example, the ground state of smaller molecules and prove a path to where, you know, decision maker could see in a few years from now, Oh, we'll be able to actually simulate not molecules we already understand, but molecules we've never been able to study a prayer, if that makes sense. And then, >>Yeah, I think there's a key point underneath that, and I think goes back to the question that you asked earlier about the why hybrid applications inherently run on the classical infrastructure and algorithms are accelerated through qs, the quantum processing units. >>And so are you sort of time sharing in the sense that this environment that you set up starts with classical, with simulation and then you get to a point where you say, okay, we're ready, you pick up the bat phone and you say I wanna, >>I would say it's more like a partnership, really. Yeah, >>Yeah. And I think, I think it's kind of the, the way I normally describe it is, you know, we've taken a look at it it from a really kind of a software development life cycle type of perspective where again, if you follow that learn experiment, pro proof of concept, and then finally productize, we, we can cover and allow for a developer to start prototyping and proofing on simulators and when they're ready all they do is flip a switch and a manifest and they can automatically engage a qu a real quantum physical quantum system. And so we've made it super simple and very accessible in a democratizing access for developers. >>Yeah. Makes such big difference. Go ahead. >>A good analogy is to like GPUs, right? Where it's not really like, you know, you send it away, but rather the GPU accelerates certain operations. The q p. Yeah, because quantum mechanics, it turns out the universe runs on linear algebra. So one way to think about the q p is the most efficient way of doing linear algebra that exists. So lots of problems that can be expressed in that form. Combinatorial optimization problems in general, certain kinds of machine learning, et cetera, get an exponential speed up by running a section of the algorithm on the quantum computer. But of course you wouldn't like port Microsoft Word. Yeah, exactly. You know, you're not gonna do that in your product. It would be a waste of your quantum computer. >>Not just that you wanna know exactly how much money is in your bank account, not probabilistically how much might be ballpark. Yeah. Realm 10, moon ballpark, right? >>10 moon ballpark. Be using that for the rest of the show. Yeah. Oh, I love that. Ken, tell me a little bit about how you identify companies and like I n Q and and end up working with Matthew. What, what's that like, >>What's it like or how do you >>Find it's the process? Like, so, you know, let's say I've got the the >>We're not going there though. Yeah. We're not >>Personal relationship. >>Well, >>You can answer these questions however you want, you know. No, but, but what does that look like for Dell? How do you, how do you curate and figure out who you're gonna bring into this partnership nest? >>Yeah, you know, I, I think it was a, it's, it was a, a very long drawn out learning opportunity. We started actually our working quantum back in 2016. So we've been at it for a long time. And only >>In quantum would we say six years is a long time. I love >>That. Exactly. >>By the way, that was like, we've been doing this for age for a >>Long time. Yeah. Very long time before >>You were born. Yes. >>Feels like it actually, believe it or not. But, so we've been at it for a long time and you know, we went down some very specific learning paths. We took a lot of different time to, to learn about different types of qubits available, different companies, what their approaches were, et cetera. Yeah. And, and we ended up meeting up with, with I N Q and, and we also have other partners as well, like ibm, but I N q you know, we, there is a nice symbiotic relationship. We're actually doing some really cool technologies that are even much, much further ahead than the, you know, strict classical does this, quantum does that where there's significant amount of interplay between the simulation systems and between the real physical QS. And so it's, it's turning out to be a great relationship. They're, they're very easy to work with and, and a lot of fun too, as you could probably tell. Yeah. >>Clearly. So before we wrap, I've got it. Okay. Okay. So get it. Let's get, let's get, yeah, let's get deep. Let's get deep for a second or a little deeper than we've been. So our current, our current understanding of all this, of the universe, it's pretty limited. It's down to the point where we effectively have it assigned to witchcraft. It's all dark energy and dark matter. Right. What does that mean exactly? Nobody knows. But if you're in the quantum computing space and you're living this every day, do you believe that it represents the key to us understanding things that currently we just can't understand classical models, including classical computing, our brains as they're constructed aren't capable of understanding the real real that's out there. Yeah. If you're in the quantum computing space, do you possess that level of hubris? Do you think that you are gonna deliver the answers? >>I'm just like, I think the more you're in the space, the more mysterious and amazing it all seems. There's a, but there is a great quote by Richard Feinman that sort of kicked off the quantum exploration. So he gave a lecture in 1981, so, you know, long before any of this began, truly ages ago, right? Yeah. And in this lecture he said, you know, kind of wild at that time, right? We had to build these giant supercomputers to simulate just a couple atoms interacting, right? And it's kind of crazy that you need all this compute to simulate what nature does with just a handful >>Particles. Yeah. >>Really small. So, and, and famously he said, you know, nature just isn't classical. Damn it. And so you need to build a computer that works with nature to understand nature. I think, you know, the, the quantum revolution has only just begun. There's so many new things to learn, and I'm sure the quantum computers of 40 years from now are not gonna look like the, you know, the computers of today, just as the classical computers of 40 years ago look quite different to us now, >>And we're a bunch of apes. But you think we'll get there? >>I, yeah, I, I mean, I, I have, I think we have, I feel incredibly optimistic that this tool, quantum computing as a tool represents a sea change in what's possible for humans to compute. >>Yeah. I think it's that possibility. You know, I, when I tell people right now in the quantum era, we're in the inac stage of the quantum era, and so we have a long way to go, but the potential is absolutely enormous. In fact, incomprehensibly enormous, I >>Was just gonna say, I don't even think we could grasp >>In the, from the inac is they had no idea of computers inside of your hand, right? Yeah. >>They're calculating, you know, trajectories, right? Yeah. If you told them, like, we'd all be video chatting, you >>Know, >>Like, and kids would be doing synchronized dances, you know, you'd be like, What? >>I love that. Well, well, on that note, Ken Matthew, really great to have you both, everyone now will be pondering the scale and scope of the universe with their 10 moon computer, 10 moons. That's right. And, and you've given me my, my new favorite bumper sticker since we've been on a, on a roll here, David and I, which is just naturally quantum. Yeah, that's, that's, that's, that's one of my new favorite phrases from the show. Thank you both for being here. David, thank you for hanging out and thank all of you for tuning in to our cube footage live here in Dallas. We are at Supercomputing. This is our last show for the day, but we look forward to seeing you tomorrow morning. My name's Savannah Peterson. Y'all have a lovely night.

>>How do y'all, and welcome to Dallas, where we're proud to be live from Supercomputing 2022. My name is Savannah Peterson, joined here by my cohost David on the Cube, and our first guest today is a very exciting visionary. He's a leader at Dell. Please welcome Travis Vhi. Travis, thank you so much for being here. >>Thank you so much for having me. >>How you feeling? >>Okay. I I'm feeling like an exciting visionary. You >>Are. That's, that's the ideas why we tee you up for that. Great. So, so tell us, Dell had some huge announcements Yes. Last night. And you get to break it to the cube audience. Give us the rundown. >>Yeah. It's a really big show for Dell. We announced a brand new suite of GPU enabled servers, eight ways, four ways, direct liquid cooling. Really the first time in the history of the portfolio that we've had this much coverage across Intel amd, Invidia getting great reviews from the show floor. I had the chance earlier to be in the whisper suite to actually look at the gear. Customers are buzzing over it. That's one thing I love about this show is the gear is here. >>Yes, it is. It is a haven for hardware nerds. Yes. Like, like well, I'll include you in this group, it sounds like, on >>That. Great. Yes. Oh >>Yeah, absolutely. And I know David is as well, sew up >>The street. Oh, big, big time. Big time hardware nerd. And just to be clear, for the kids that will be watching these videos Yes. We're not talking about alien wear gaming systems. >>No. Right. >>So they're >>Yay big yay tall, 200 pounds. >>Give us a price point on one of these things. Re retail, suggested retail price. >>Oh, I'm >>More than 10 grand. >>Oh, yeah. Yeah. Try another order of magnitude. Yeah. >>Yeah. So this is, this is the most exciting stuff from an infrastructure perspective. Absolutely. You can imagine. Absolutely. But what is it driving? So talk, talk to us about where you see the world of high performance computing with your customers. What are they, what are they doing with this? What do they expect to do with this stuff in the future? >>Yeah. You know, it's, it's a real interesting time and, and I know that the provenance of this show is HPC focused, but what we're seeing and what we're hearing from our customers is that AI workloads and traditional HPC workloads are becoming almost indistinguishable. You need the right mix of compute, you need GPU acceleration, and you need the ability to take the vast quantities of data that are being generated and actually gather insight from them. And so if you look at what customers are trying to do with, you know, enterprise level ai, it's really, you know, how do I classify and categorize my data, but more, more importantly, how do I make sense of it? How do I derive insights from it? Yeah. And so at the end of the day, you know, you look, you look at what customers are trying to do. It's, it's take all the various streams of data, whether it be structured data, whether it be unstructured data, bring it together and make decisions, make business decisions. >>And it's a really exciting time because customers are saying, you know, the same things that, that, that, you know, research scientists and universities have been trying to do forever with hpc. I want to do it on industrial scale, but I want to do it in a way that's more open, more flexible, you know, I call it AI for the rest of us. And, and, and customers are here and they want those systems, but they want the ecosystem to support ease of deployment, ease of use, ease of scale. And that's what we're providing in addition to the systems. We, we provide, you know, Dell's one of the only providers on the on in the industry that can provide not only the, the compute, but the networking and the storage, and more importantly, the solutions that bring it all together. Give you one example. We, we have what we call a validated design for, for ai. And that validated design, we put together all of the pieces, provided the recipe for customers so that they can take what used to be two months to build and run a model. We provide that capability 18 times faster. So we're talking about hours versus months. So >>That's a lot. 18 times faster. I just wanna emphasize that 18 times faster, and we're talking about orders of magnitude and whatnot up here, that makes a huge difference in what people are able to do. Absolutely. >>Absolutely. And so, I mean, we've, you know, you've been doing this for a while. We've been talking about the, the deluge of data forever, but it's gotten to the point and it's, you know, the, the disparity of the data, the fact that much of it remains siloed. Customers are demanding that we provide solutions that allow them to bring that data together, process it, make decisions with it. So >>Where, where are we in the adoption cycle early because we, we've been talking about AI and ML for a while. Yeah. You, you mentioned, you know, kind of the leading edge of academia and supercomputing and HPC and what that, what that conjures up in people's minds. Do you have any numbers or, you know, any, any thoughts about where we are in this cycle? How many, how many people are actually doing this in production versus, versus experimenting at this point? Yeah, >>I think it's a, it's a reason. There's so much interest in what we're doing and so much demand for not only the systems, but the solutions that bring the systems together. The ecosystem that brings the, the, the systems together. We did a study recently and ask customers where they felt they were at in terms of deploying best practices for ai, you know, mass deployment of ai. Only 31% of customers said that they felt that they self-reported. 31% said they felt that they were deploying best practices for their AI deployments. So almost 70% self reporting saying we're not doing it right yet. Yeah. And, and, and another good stat is, is three quarters of customers have fewer than five AI applications deployed at scale in their, in their IT environments today. So, you know, I think we're on the, you know, if, if I, you think about it as a traditional S curve, I think we're at the first inflection point and customers are asking, Can I do it end to end? >>Can I do it with the best of breed in terms of systems? But Dell, can you also use an ecosystem that I know and understand? And I think that's, you know, another great example of something that Dell is doing is, is we have focused on ethernet as connectivity for many of the solutions that we put together. Again, you know, provenance of hpc InfiniBand, it's InfiniBand is a great connectivity option, but you know, there's a lot of care and feeding that goes along with InfiniBand and the fact that you can do it both with InfiniBand for those, you know, government class CU scale, government scale clusters or university scale clusters and more of our enterprise customers can do it with, with ethernet on premises. It's a great option. >>Yeah. You've got so many things going on. I got to actually check out the million dollar hardware that you have just casually Yeah. Sitting in your booth. I feel like, I feel like an event like this is probably one of the only times you can let something like that out. Yeah, yeah. And, and people would actually know what it is you're working >>With. We actually unveiled it. There was a sheet on it and we actually unveiled it last night. >>Did you get a lot of uz and os >>You know, you said this was a show for hardware nerds. It's been a long time since I've been at a shoe, a show where people cheer and u and a when you take the sheet off the hardware and, and, and Yes, yes, >>Yes, it has and reveal you had your >>Moment. Exactly, exactly. Our three new systems, >>Speaking of u and os, I love that. And I love that everyone was excited as we all are about it. What I wanna, It's nice to be home with our nerds. Speaking of, of applications and excitement, you get to see a lot of different customers across verticals. Is there a sector or space that has you personally most excited? >>Oh, personally most excited, you know, for, for credibility at home when, when the sector is media and entertainment and the movie is one that your, your children have actually seen, that one gives me credibility. Exciting. It's, you can talk to your friends about it at, at at dinner parties and things like that. I'm like, >>Stuff >>Curing cancer. Marvel movie at home cred goes to the Marvel movie. Yeah. But, but, but you know, what really excites me is the variety of applications that AI is being used, used in healthcare. You know, on a serious note, healthcare, genomics, a huge and growing application area that excites me. You know, doing, doing good in the world is something that's very important to Dell. You know, know sustainability is something that's very important to Dell. Yeah. So any application related to that is exciting to me. And then, you know, just pragmatically speaking, anything that helps our customers make better business decisions excites me. >>So we are, we are just at the beginning of what I refer to as this rolling thunder of cpu. Yes. Next generation releases. We re recently from AMD in the near future it'll be, it'll be Intel joining the party Yeah. Going back and forth, back and forth along with that gen five PCI e at the motherboard level. Yep. It's very easy to look at it and say, Wow, previous gen, Wow, double, double, double. It >>Is, double >>It is. However, most of your customers, I would guess a fair number of them might be not just N minus one, but n minus two looking at an upgrade. So for a lot of people, the upgrade season that's ahead of us is going to be not a doubling, but a four x or eight x in a lot of, in a lot of cases. Yeah. So the quantity of compute from these new systems is going to be a, it's gonna be a massive increase from where we've been in, in, in the recent past, like as in last, last Tuesday. So is there, you know, this is sort of a philosophical question. We talked a little earlier about this idea of the quantitative versus qualitative difference in computing horsepower. Do we feel like we're at a point where there's gonna be an inflection in terms of what AI can actually deliver? Yeah. Based on current technology just doing it more, better, faster, cheaper? Yeah. Or do we, or do we need this leap to quantum computing to, to get there? >>Yeah. I look, >>I think we're, and I was having some really interesting conversations with, with, with customers that whose job it is to run very, very large, very, very complex clusters. And we're talking a little bit about quantum computing. Interesting thing about quantum computing is, you know, I think we're or we're a ways off still. And in order to make quantum computing work, you still need to have classical computing surrounding Right. Number one. Number two, with, with the advances that we're, we're seeing generation on generation with this, you know, what, what has moved from a kind of a three year, you know, call it a two to three year upgrade cycle to, to something that because of all of the technology that's being deployed into the industry is almost more continuous upgrade cycle. I, I'm personally optimistic that we are on the, the cusp of a new level of infrastructure modernization. >>And it's not just the, the computing power, it's not just the increases in GPUs. It's not, you know, those things are important, but it's things like power consumption, right? One of the, the, the ways that customers can do better in terms of power consumption and sustainability is by modernizing infrastructure. Looking to your point, a lot of people are, are running n minus one, N minus two. The stuff that's coming out now is, is much more energy efficient. And so I think there's a lot of, a lot of vectors that we're seeing in, in the market, whether it be technology innovation, whether it be be a drive for energy efficiency, whether it be the rise of AI and ml, whether it be all of the new silicon that's coming in into the portfolio where customers are gonna have a continuous reason to upgrade. I mean, that's, that's my thought. What do you think? >>Yeah, no, I think, I think that the, the, the objective numbers that are gonna be rolling out Yeah. That are starting to roll out now and in the near future. That's why it's really an exciting time. Yeah. I think those numbers are gonna support your point. Yeah. Because people will look and they'll say, Wait a minute, it used to be a dollar, but now it's $2. That's more expensive. Yeah. But you're getting 10 times as much Yeah. For half of the amount of power boom. And it's, and it's >>Done. Exactly. It's, it's a >>Tco It's, it's no brainer. It's Oh yeah. You, it gets to the point where it's, you look at this rack of amazing stuff that you have a personal relationship with and you say, I can't afford to keep you plugged in anymore. Yeah. >>And Right. >>The power is such a huge component of this. Yeah. It's huge, huge. >>Our customer, I mean, it's always a huge issue, but our customers, especially in Amia with what's going on over there are, are saying, I, you know, I need to upgrade because I need to be more energy efficient. >>Yeah. >>Yeah. I I, we were talking about 20 years from now, so you've been at Dell over 18 years. >>Yeah. It'll be 19 in in May. >>Congratulations. Yeah. What, what commitment, so 19 years from now in your, in your second Dell career. Yeah. What are we gonna be able to say then that perhaps we can't say now? >>Oh my gosh. Wow. 19 years from now. >>Yeah. I love this as an arbitrary number too. This is great. Yeah. >>38 year Dell career. Yeah. >>That might be a record. Yeah. >>And if you'd like to share the winners of Super Bowls and World Series in advance, like the world and the, the sports element act from back to the future. So we can play ball bets power and the >>Power ball, but, but any >>Point building Yeah. I mean this is what, what, what, what do you think ai, what's AI gonna deliver in the next decade? >>Yeah. I, I look, I mean, there are are, you know, global issues that advances in computing power will help us solve. And, you know, the, the models that are being built, the ability to generate a, a digital copy of the analog world and be able to run models and simulations on it is, is amazing. Truly. Yeah. You know, I, I was looking at some, you know, it's very, it's a very simple and pragmatic thing, but I think it's, it, it's an example of, of what could be, we were with one of our technology providers and they, they were, were showing us a digital simulation, you know, a digital twin of a factory for a car manufacturer. And they were saying that, you know, it used to be you had to build the factory, you had to put the people in the factory. You had to, you know, run cars through the factory to figure out sort of how you optimize and you know, where everything's placed. >>Yeah. They don't have to do that anymore. No. Right. They can do it all via simulation, all via digital, you know, copy of, of analog reality. And so, I mean, I think the, you know, the, the, the, the possibilities are endless. And, you know, 19 years ago, I had no idea I'd be sitting here so excited about hardware, you know, here we are baby. I think 19 years from now, hardware still matters. Yeah. You know, hardware still matters. I know software eats the world, the hardware still matters. Gotta run something. Yeah. And, and we'll be talking about, you know, that same type of, of example, but at a broader and more global scale. Well, I'm the knucklehead who >>Keeps waving his phone around going, There's one terabyte in here. Can you believe that one terabyte? Cause when you've been around long enough, it's like >>Insane. You know, like, like I've been to nasa, I live in Texas, I've been to NASA a couple times. They, you know, they talk about, they sent, you know, they sent people to the moon on, on way less, less on >>Too far less in our pocket computers. Yeah. It's, it's amazing. >>I am an optimist on, on where we're going clearly. >>And we're clearly an exciting visionary, like we said, said the gate. It's no surprise that people are using Dell's tech to realize their AI ecosystem dreams. Travis, thank you so much for being here with us David. Always a pleasure. And thank you for tuning in to the Cube Live from Dallas, Texas. My name is Savannah Peterson. We'll be back with more supercomputing soon.