(inspirational music) >> The history of high performance computing is unique and storied. You know, it's generally accepted that the first true supercomputer was shipped in the mid 1960s by Controlled Data Corporations, CDC, designed by an engineering team led by Seymour Cray, the father of Supercomputing. He left CDC in the 70's to start his own company, of course, carrying his own name. Now that company Cray, became the market leader in the 70's and the 80's, and then the decade of the 80's saw attempts to bring new designs, such as massively parallel systems, to reach new heights of performance and efficiency. Supercomputing design was one of the most challenging fields, and a number of really brilliant engineers became kind of quasi-famous in their little industry. In addition to Cray himself, Steve Chen, who worked for Cray, then went out to start his own companies. Danny Hillis, of Thinking Machines. Steve Frank of Kendall Square Research. Steve Wallach tried to build a mini supercomputer at Convex. These new entrants, they all failed, for the most part because the market at the time just wasn't really large enough and the economics of these systems really weren't that attractive. Now, the late 80's and the 90's saw big Japanese companies like NEC and Fujitsu entering the fray and governments around the world began to invest heavily in these systems to solve societal problems and make their nations more competitive. And as we entered the 21st century, we saw the coming of petascale computing, with China actually cracking the top 100 list of high performance computing. And today, we're now entering the exascale era, with systems that can complete a billion, billion calculations per second, or 10 to the 18th power. Astounding. And today, the high performance computing market generates north of $30 billion annually and is growing in the high single digits. Supercomputers solve the world's hardest problems in things like simulation, life sciences, weather, energy exploration, aerospace, astronomy, automotive industries, and many other high value examples. And supercomputers are expensive. You know, the highest performing supercomputers used to cost tens of millions of dollars, maybe $30 million. And we've seen that steadily rise to over $200 million. And today we're even seeing systems that cost more than half a billion dollars, even into the low billions when you include all the surrounding data center infrastructure and cooling required. The US, China, Japan, and EU countries, as well as the UK, are all investing heavily to keep their countries competitive, and no price seems to be too high. Now, there are five mega trends going on in HPC today, in addition to this massive rising cost that we just talked about. One, systems are becoming more distributed and less monolithic. The second is the power of these systems is increasing dramatically, both in terms of processor performance and energy consumption. The x86 today dominates processor shipments, it's going to probably continue to do so. Power has some presence, but ARM is growing very rapidly. Nvidia with GPUs is becoming a major player with AI coming in, we'll talk about that in a minute. And both the EU and China are developing their own processors. We're seeing massive densities with hundreds of thousands of cores that are being liquid-cooled with novel phase change technology. The third big trend is AI, which of course is still in the early stages, but it's being combined with ever larger and massive, massive data sets to attack new problems and accelerate research in dozens of industries. Now, the fourth big trend, HPC in the cloud reached critical mass at the end of the last decade. And all of the major hyperscalers are providing HPE, HPC as a service capability. Now finally, quantum computing is often talked about and predicted to become more stable by the end of the decade and crack new dimensions in computing. The EU has even announced a hybrid QC, with the goal of having a stable system in the second half of this decade, most likely around 2027, 2028. Welcome to theCUBE's preview of SC22, the big supercomputing show which takes place the week of November 13th in Dallas. theCUBE is going to be there. Dave Nicholson will be one of the co-hosts and joins me now to talk about trends in HPC and what to look for at the show. Dave, welcome, good to see you. >> Hey, good to see you too, Dave. >> Oh, you heard my narrative up front Dave. You got a technical background, CTO chops, what did I miss? What are the major trends that you're seeing? >> I don't think you really- You didn't miss anything, I think it's just a question of double-clicking on some of the things that you brought up. You know, if you look back historically, supercomputing was sort of relegated to things like weather prediction and nuclear weapons modeling. And these systems would live in places like Lawrence Livermore Labs or Los Alamos. Today, that requirement for cutting edge, leading edge, highest performing supercompute technology is bleeding into the enterprise, driven by AI and ML, artificial intelligence and machine learning. So when we think about the conversations we're going to have and the coverage we're going to do of the SC22 event, a lot of it is going to be looking under the covers and seeing what kind of architectural things contribute to these capabilities moving forward, and asking a whole bunch of questions. >> Yeah, so there's this sort of theory that the world is moving toward this connectivity beyond compute-centricity to connectivity-centric. We've talked about that, you and I, in the past. Is that a factor in the HPC world? How is it impacting, you know, supercomputing design? >> Well, so if you're designing an island that is, you know, tip of this spear, doesn't have to offer any level of interoperability or compatibility with anything else in the compute world, then connectivity is important simply from a speeds and feeds perspective. You know, lowest latency connectivity between nodes and things like that. But as we sort of democratize supercomputing, to a degree, as it moves from solely the purview of academia into truly ubiquitous architecture leverage by enterprises, you start asking the question, "Hey, wouldn't it be kind of cool if we could have this hooked up into our ethernet networks?" And so, that's a whole interesting subject to explore because with things like RDMA over converged ethernet, you now have the ability to have these supercomputing capabilities directly accessible by enterprise computing. So that level of detail, opening up the box of looking at the Nix, or the storage cards that are in the box, is actually critically important. And as an old-school hardware knuckle-dragger myself, I am super excited to see what the cutting edge holds right now. >> Yeah, when you look at the SC22 website, I mean, they're covering all kinds of different areas. They got, you know, parallel clustered systems, AI, storage, you know, servers, system software, application software, security. I mean, wireless HPC is no longer this niche. It really touches virtually every industry, and most industries anyway, and is really driving new advancements in society and research, solving some of the world's hardest problems. So what are some of the topics that you want to cover at SC22? >> Well, I kind of, I touched on some of them. I really want to ask people questions about this idea of HPC moving from just academia into the enterprise. And the question of, does that mean that there are architectural concerns that people have that might not be the same as the concerns that someone in academia or in a lab environment would have? And by the way, just like, little historical context, I can't help it. I just went through the upgrade from iPhone 12 to iPhone 14. This has got one terabyte of storage in it. One terabyte of storage. In 1997, I helped build a one terabyte NAS system that a government defense contractor purchased for almost $2 million. $2 million! This was, I don't even know, it was $9.99 a month extra on my cell phone bill. We had a team of seven people who were going to manage that one terabyte of storage. So, similarly, when we talk about just where are we from a supercompute resource perspective, if you consider it historically, it's absolutely insane. I'm going to be asking people about, of course, what's going on today, but also the near future. You know, what can we expect? What is the sort of singularity that needs to occur where natural language processing across all of the world's languages exists in a perfect way? You know, do we have the compute power now? What's the interface between software and hardware? But really, this is going to be an opportunity that is a little bit unique in terms of the things that we typically cover, because this is a lot about cracking open the box, the server box, and looking at what's inside and carefully considering all of the components. >> You know, Dave, I'm looking at the exhibitor floor. It's like, everybody is here. NASA, Microsoft, IBM, Dell, Intel, HPE, AWS, all the hyperscale guys, Weka IO, Pure Storage, companies I've never heard of. It's just, hundreds and hundreds of exhibitors, Nvidia, Oracle, Penguin Solutions, I mean, just on and on and on. Google, of course, has a presence there, theCUBE has a major presence. We got a 20 x 20 booth. So, it's really, as I say, to your point, HPC is going mainstream. You know, I think a lot of times, we think of HPC supercomputing as this just sort of, off in the eclectic, far off corner, but it really, when you think about big data, when you think about AI, a lot of the advancements that occur in HPC will trickle through and go mainstream in commercial environments. And I suspect that's why there are so many companies here that are really relevant to the commercial market as well. >> Yeah, this is like the Formula 1 of computing. So if you're a Motorsports nerd, you know that F1 is the pinnacle of the sport. SC22, this is where everybody wants to be. Another little historical reference that comes to mind, there was a time in, I think, the early 2000's when Unisys partnered with Intel and Microsoft to come up with, I think it was the ES7000, which was supposed to be the mainframe, the sort of Intel mainframe. It was an early attempt to use... And I don't say this in a derogatory way, commodity resources to create something really, really powerful. Here we are 20 years later, and we are absolutely smack in the middle of that. You mentioned the focus on x86 architecture, but all of the other components that the silicon manufacturers bring to bear, companies like Broadcom, Nvidia, et al, they're all contributing components to this mix in addition to, of course, the microprocessor folks like AMD and Intel and others. So yeah, this is big-time nerd fest. Lots of academics will still be there. The supercomputing.org, this loose affiliation that's been running these SC events for years. They have a major focus, major hooks into academia. They're bringing in legit computer scientists to this event. This is all cutting edge stuff. >> Yeah. So like you said, it's going to be kind of, a lot of techies there, very technical computing, of course, audience. At the same time, we expect that there's going to be a fair amount, as they say, of crossover. And so, I'm excited to see what the coverage looks like. Yourself, John Furrier, Savannah, I think even Paul Gillin is going to attend the show, because I believe we're going to be there three days. So, you know, we're doing a lot of editorial. Dell is an anchor sponsor, so we really appreciate them providing funding so we can have this community event and bring people on. So, if you are interested- >> Dave, Dave, I just have- Just something on that point. I think that's indicative of where this world is moving when you have Dell so directly involved in something like this, it's an indication that this is moving out of just the realm of academia and moving in the direction of enterprise. Because as we know, they tend to ruthlessly drive down the cost of things. And so I think that's an interesting indication right there. >> Yeah, as do the cloud guys. So again, this is mainstream. So if you're interested, if you got something interesting to talk about, if you have market research, you're an analyst, you're an influencer in this community, you've got technical chops, maybe you've got an interesting startup, you can contact David, david.nicholson@siliconangle.com. John Furrier is john@siliconangle.com. david.vellante@siliconangle.com. I'd be happy to listen to your pitch and see if we can fit you onto the program. So, really excited. It's the week of November 13th. I think November 13th is a Sunday, so I believe David will be broadcasting Tuesday, Wednesday, Thursday. Really excited. Give you the last word here, Dave. >> No, I just, I'm not embarrassed to admit that I'm really, really excited about this. It's cutting edge stuff and I'm really going to be exploring this question of where does it fit in the world of AI and ML? I think that's really going to be the center of what I'm really seeking to understand when I'm there. >> All right, Dave Nicholson. Thanks for your time. theCUBE at SC22. Don't miss it. Go to thecube.net, go to siliconangle.com for all the news. This is Dave Vellante for theCUBE and for Dave Nicholson. Thanks for watching. And we'll see you in Dallas. (inquisitive music)

>>The cube presents HPE discover 2022 brought to you by HPE. >>Hey everyone. Welcome back to the Cube's coverage of HPE. Discover 22 live from the Sans expo center in Las Vegas. Lisa Martin, here with Dave Velante. We've an alumni back joining us to talk about high performance computing and AI, Justin ARD, EVP, and general manager of HPC and AI at HPE. That's a mouthful. Welcome back. >>It is no, it's great to be back and wow, it's great to be back in person as well. >>It's it's life changing to be back in person. The keynote this morning was great. The Dave was saying the energy that he's seen is probably the most out of, of any discover that you've been at and we've been feeling that and it's only day one. >>Yeah, I, I, I agree. And I think it's a Testament to the places in the market that we're leading the innovation we're driving. I mean, obviously the leadership in HPE GreenLake and, and enabling as a service for, for every customer, not just those in the public cloud, providing that, that capability. And then obviously what we're doing at HPC and AI breaking, uh, you know, breaking records and, uh, advancing the industry. So >>I just saw the Q2 numbers, nice revenue growth there for HPC and AI. Talk to us about the lay of the land what's going on, what are customers excited about? >>Yeah. You know, it's, it's a, it's a really fascinating time in this, in this business because we're, you know, we just, we just delivered the first, the world's first exo scale system. Right. And that's, uh, you know, that's a huge milestone for our industry, a breakthrough, you know, 13 years ago, we did the first Petta scale system. Now we're doing the first exo scale system, huge advance forward. But what's exciting too, is these systems are enabling new applications, new workloads, breakthroughs in AI, the beginning of being able to do proper quantum simulations, which will lead us to a much, you know, brighter future with quantum and then actually better and more granular models, which have the ability to really change the world. >>I was telling Lisa that during the pandemic we did, uh, exo scale day, it was like this co yep. You know, produce event. And we weren't quite at exo scale yet, but we could see it coming. And so it was great to see in frontier and, and the keynote you guys broke through that, is that a natural evolution of HPC or is this we entering a new era? >>Yeah, I, I think it's a new era and I think it's a new era for a few reasons because that, that breakthrough really, it starts to enable a different class of use cases. And it's combined with the fact that I think, you know, you look at where the rest of the enterprises data set has gone, right? We've got a lot more data, a lot more visibility to data. Um, but we don't know how to use it. And now with this computing power, we can start to create new insights and new applications. And so I think this is gonna be a path to making HPC more broadly available. And of course it introduces AI models at scale. And that's, that's really critical cause AI is a buzzword. I mean, lots of people say they're doing AI, but when you know, to, to build true intelligence, not, not effectively, you know, a machine that learns data and then can only handle that data, but to build true intelligence where you've got something that can continue to learn and understand and grow and evolve, you need this class of system. And so I think we're at, we're at the forefront of a lot of exciting innovation. H how, >>In terms of innovation, how important is it that you're able to combine as a service and HPC? Uh, what does that mean for, for customers for experimentation and innovation? >>You know, a couple things I've been, I've actually been talking to customers about that over the last day and a half. And, you know, one is, um, you think about these, these systems are, they're very large and, and they're, they're pretty, you know, pretty big bets if you're a customer. So getting early access to them right, is, is really key, making sure that they're, they can migrate their software, their applications, again, in our space, most of our applications are custom built, whether you're a, you know, a government or a private sector company, that's using these systems, you're, you're doing these are pretty specialized. So getting that early access is important. And then actually what we're seeing is, uh, with the growth and explosion of insight that we can enable. And some of the diversity of, you know, new, um, accelerator partners and new processors that are on the market is actually the attraction of diversity. And so making things available where customers can use multimodal systems. And we've seen that in this era, like our customer Lumi and Finland number, the number three fastest system in the world actually has two sides to their system. So there's a compute side, dense compute side and a dense accelerator side. >>So Oak Ridge national labs was on stage with Antonio this morning, the, the talking about frontier, the frontier system, I thought what a great name, very apropo, but it was also just named the number one to the super computing, top 500. That's a pretty big accomplishment. Talk about the impact of what that really means. >>Yeah. I, I think a couple things, first of all, uh, anytime you have this breakthrough of number one, you see a massive acceleration of applications. And if you really, if you look at the applications that were built, because when the us department of energy funded these Exoscale products or platforms, they also funded app a set of applications. And so it's the ability to get more accurate earth models for long term climate science. It's the ability to model the electrical grid and understand better how to build resiliency into that grid. His ability is, um, Dr. Te Rossi talked about a progressing, you know, cancer research and cancer breakthroughs. I mean, there's so many benefits to the world that we can bring with these systems. That's one element. The other big part of this breakthrough is actually a list, a lesser known list from the top 500 called the green 500. >>And that's where we measure performance over power consumption. And what's a huge breakthrough in this system. Is that not only to frontier debut at number one on the top 500, it's actually got the top two spots, uh, because it's got a small test system that also is up there, but it's got the top two spots on the green 500 and that's actually a real huge breakthrough because now we're doing a ton more computation at far lesser power. And that's really important cuz you think about these systems, ultimately you can, you can't, you know, continue to consume power linearly with scaling up performance. There's I mean, there's a huge issue on our impact on our environment, but it's the impact to the power grid. It's the impact to heat dissipation. There's a lot of complexities. So this breakthrough with frontier also enables us no pun intended to really accelerate, you know, the, the capacity and scale of these systems and what we can deliver. >>It feels like we're entering a new Renaissance of HPC. I mean, I'm old enough to remember. I, it was, it wasn't until recently my wife, not recently, maybe five, six years ago, my wife threw out my, my green thinking machines. T-shirt that Danny Hillis gave you guys probably both too young to remember, but you had thinking machines, Ken to square research convex tried to mini build a mini computer HPC. Okay. And there was a lot of innovation going on around that time and then it just became too expensive and, and, and other things X 86 happened. And, and, but it feels like now we're entering a, a new era of, of HPC. Is that valid or is it true? What's that mean for HPC as an industry and for industry? >>Yeah, I think, I think it's a BR I think it's a breadth. Um, it's a market that's opening and getting much more broader the number of applications you can run, you know, and we've traditionally had, you know, scientific applications, obviously there's a ton in energy and, and you know, physics and some of the traditional areas that obviously the department of energy sponsor, but, you know, we saw this with, with even the COVID pandemic, right? Our, our supercomputers were used to identify the spike protein to, to help and validate and test these vaccines and bring them to market and record time. We saw some of the benefits of these breakthroughs. And I think it's this combination of that, that we actually have the data, you know, it's, it's digital, it's captured, um, we're capturing it at, you know, at the edge, we're capturing it and, and storing it obviously more broadly. So we have the access to the data and now we have the compute power to run it. And the other big thing is the techniques around artificial intelligence. I mean, what we're able to do with neural networks, computer vision, large language models, natural language processing. These are breakthroughs that, um, one require these large systems, but two, as you give them a large systems, you can actually really enable acceleration of how sophisticated these, these applications can get. >>Let's talk about the impact of the convergence of HPC and AI. What are some of the things that you're seeing now and what are some of the things that we're gonna see? >>Yeah. So, so I, one thing I like to talk about is it's, it's really, it's not a convergence. I think it's it. Sometimes it gets a little bit oversimplified. It's actually, it's traditional modeling and simulation leveraging machine learning to, to refine the simulation. And this is a, is one of the things we talk about a lot in AI, right? It's using machine learning to actually create code in real time, rather than humans doing it, that ability to refine the model as you're running. So we have an example. We did a, uh, we, we actually launched an open source solution called smart SIM. And the first application of that was climate science. And it's what it's doing is it's actually learning the data from the model as the simulation is running to provide more accurate climate prediction. But you think about that, that could be run for, you know, anything that has a complex model. >>You could run that for financial modeling, you can use AI. And so we're seeing things like that. And I think we'll continue to see that the other side of that is using modeling and simulation to actually represent what you see in AI. So we were talking about the grid. This is one of the Exoscale compute projects you could actually use once you actually get, get the data and you can start modeling the behavior of every electrical endpoint in a city. You know, the, the meter in your house, the substation, the, the transformers, you can start measuring the FX of that. You can then build equations. Well, once you build those equations, you can then take a model, cuz you've learned what actually happens in the real world, build the equation. And then you can provide that to someone who doesn't need a extra scale supercomputer to run it, but that, you know, your local energy company can better understand what's happening and they'll know, oh, there's a problem here. We need to shift the grid or respond more, more dynamically. And hopefully that avoids brownouts or, you know, some of the catastrophic outages we've >>Seen so they can deploy that model, which, which inherently has that intelligence on sort of more cost effective systems and then apply it to a much broader range. Do any of those, um, smart simulations on, on climate suggest that it's, it's all a hoax. You don't have to answer that question. <laugh> um, what, uh, >>The temperature outside Dave might, might give you a little bit of an argument to that. >>Tell us about quantum, what's your point of view there? Is it becoming more stable? What's H HPE doing there? >>Yeah. So, so look, I think there's, there's two things to understand with quantum there's quantum hardware, right? Fundamentally, um, how, um, how that runs very differently than, than how we run traditional computers. And then there's the applications. And ultimately a quantum application on quantum hardware will be far more efficient in the future than, than anything else. We, we see the opportunity for, uh, much like we see with, you know, with HPC and AI, we just talked about for quantum to be complimentary. It runs really well with certain applications that fabricate themselves as quantum problems and some great examples are, you know, the, the life sciences, obviously quantum chemistry, uh, you see some, actually some opportunities in, in, uh, in AI and in other areas where, uh, quantum has a very, very, it, it just lends itself more naturally to the behavior of the problem. And what we believe is that in the short term, we can actually model quantum effectively on these, on these super computers, because there's not a perfect quantum hardware replacement over time. You know, we would anticipate that will evolve and we'll see quantum accelerators much. Like we see, you know, AI accelerators today in this space. So we think it's gonna be a natural evolution in progression, but there's certain applications that are just gonna be solved better by quantum. And that's the, that's the future we'll we'll run into. And >>You're suggesting if I understood it correctly, you can start building those applications and, and at least modeling what those applications look like today with today's technology. That's interesting because I mean, I, I think it's something rudimentary compared to quantum as flash storage, right? When you got rid of the spinning disc, it changed the way in which people thought about writing applications. So if I understand it, new applications that can take advantage of quantum are gonna change the way in which developers write, not one or a zero it's one and virtually infinite <laugh> combinations. >>Yeah. And I actually, I think that's, what's compelling about the opportunity is that you can, if you think about a lot of traditional the traditional computing industry, you always had to kind of wait for the hardware to be there, to really write, write, and test the application. And we, you know, we even see that with our customers and HPC and, and AI, right? They, they build a model and then they, they actually have to optimize it across the hardware once they deploy it at scale. And with quantum what's interesting is you can actually, uh, you can actually model and, and, and make progress on the software. And then, and then as the hardware becomes available, optimize it. And that's, you know, that's why we see this. We talk about this concept of quantum accelerators as, as really interesting, >>What are the customer conversations these days as there's been so much evolution in HPC and AI and the technology so much change in the world in the last couple of years, is it elevating up the CS stack in terms of your conversations with customers wanting to become familiar with Exoscale computing? For example? >>Yeah. I, I think two things, uh, one, one is we see a real rise in digital sovereignty and Exoscale and HPC as a core fund, you know, fundamental foundation. So you see what, um, you know, what Europe is doing with the, the, the Euro HPC initiative, as one example, you know, we see the same kind of leadership coming out of the UK with the system. We deployed with them in Archer two, you know, we've got many customers across the globe deploying next generation weather forecasting systems, but everybody feels, they, they understand the foundation of having a strong supercomputing and HPC capability and competence and not just the hardware, the software development, the scientific research, the, the computational scientists to enable them to remain competitive economically. It's important for defense purposes. It's important for, you know, for helping their citizens, right. And providing, you know, providing services and, and betterment. >>So that's one, I'd say that's one big theme. The other one is something Dave touched on before around, you know, as a service and why we think HP GreenLake will be, uh, a beautiful marriage with our, with our HPC and AI systems over time, which is customers also, um, are going to scale up and build really complex models. And then they'll simplify them and deploy them in other places. And so there's a number of examples. We see them, you know, we see them in places like oil and gas. We see them in manufacturing where I've gotta build a really complex model, figure out what it looks like. Then I can reduce it to a, you know, to a, uh, certain equation or application that I can then deploy. So I understand what's happening and running because you, of course, as much as I would love it, you're not gonna have, uh, every enterprise around the world or every endpoint have an exit scale system. Right. So, so that ability to, to, to really provide an as a service element with HP GreenLake, we think is really compelling. >>HP's move into HPC, the acquisitions you've made it really have become a differentiator for the company. Hasn't it? >>Yeah. And I, and I think what's unique about us today. If you look at the landscape is we're, we're really the only system provider globally. Yeah. You know, there are, there are local players that we compete with. Um, but we are the one true global system provider. And we're also the only, I would say the only holistic innovator at the system level to, to, you know, to credit my team on the work they're doing. But, you know, we're, we're also very committed to open standards. We're investing in, um, you know, in a number of places where we contribute the dev the software assets to open source, we're doing work with standards bodies to progress and accelerate the industry and enable the ecosystem. And, uh, and I think that, you know, ultimately the, the, the last thing I'd say is we, we are so connected in, um, with, through our, through the legacy or the, the legend of H Hewlett Packard labs, which now also reports into me that we have these really tight ties into advanced research and that some of that advanced research, which isn't just, um, around kind of core processing Silicon is really critical to enabling better applications, better use cases and accelerating the outcomes we see in these systems going forward. >>Can >>You double click on that? I, I, I wasn't aware that kind of reported into your group. Yeah. So, you know, the roots of HP are invent, right? Yeah. HP labs are, are renowned. It kinda lost that formula for a while. And now it's sounds like it's coming back. What, what, what are some of the cool things that you guys are working on? Well, >>You know, let me, let me start with a little bit of recent history. So we just talked about the exo scale program. I mean, that was a, that's a great example of where we had a public private partnership with the department of energy and it, and it wasn't just that we, um, you know, we built a system and delivered it, but if you go back a decade ago, or five years ago, there were, there were innovations that were built, you know, to accelerate that system. One is our Slingshot fabric as an example, which is a core enable of, of acceler, you know, of, of this accelerated computing environment, but others in software applications and services that allowed us to, you know, to really deliver a, a complete solution into the market. Um, today we're looking at things around trustworthy and ethical AI, so trustworthy AI in the sense that, you know, the models are accurate, you know, and that's, that's a challenge on two dimensions, cuz one is the, model's only as good as the data it's studying. >>So you need to validate that the data's accurate and then you need to really study how, you know, how do I make sure that even if the data is accurate, I've got a model that then, you know, is gonna predict the right things and not call a, a dog, a cat, or a, you know, a, a cat, a mouse or whatever that is. But so that's important. And, uh, so that's one area. The other is future system architectures because, um, as we've talked about before, Dave, you have this constant tension between the fabric, uh, you know, the interconnect, the compute and the, and the storage and, you know, constant, constantly balancing it. And so we're really looking at that, how do we do more, you know, shared memory access? How do we, you know, how do we do more direct rights? Like, you know, looking at some future system architectures and thinking about that. And we, you know, we think that's really, really critical in this part of the business because these heterogeneous systems, and not saying I'm gonna have one monolithic application, but I'm gonna have applications that need to take advantage of different code, different technologies at different times. And being able to move that seamlessly across the architecture, uh, we think is gonna be the, you know, a part of the, the hallmark of the Exoscale era, including >>Edge, which is a completely different animal. I think that's where some disruption is gonna gonna bubble up here in the next decade. >>So, yeah know, and, and that's, you know, that's the last thing I'd say is, is we look at AI at scale, which is another core part of the business that can run on these large clusters. That means getting all the way down to the edge and doing inference at scale, right. And, and inference at scale is, you know, I, I was, um, about a month ago, I was at the world economic forum. We were talking about the space economy and it's a great, you know, to me, it's the perfect example of inference, because if you get a set of data that you know, is, is out at Mars, it doesn't matter whether, you know, whether you wanna push all that data back to, uh, to earth for processing or not. You don't really have a choice, cuz it's just gonna take too long. >>Don't have that time. Justin, thank you so much for spending some of your time with Dave and me talking about what's going on with HBC and AI. The frontier just seems endless and very exciting. We appreciate your time on your insights. >>Great. Thanks so much. Thanks. >>Yes. And don't call a dog, a cat that I thought I learned from you. A dog at no, Nope. <laugh> Nope. <laugh> for Justin and Dave ante. I'm Lisa Martin. 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