>>Welcome back, everyone live here at Supercomputing 22 in Dallas, Texas. I'm John for host of the Queue here at Paul Gillin, editor of Silicon Angle, getting all the stories, bringing it to you live. Supercomputer TV is the queue right now. And bringing all the action Bresniker, chief architect of Hewlett Packard Labs with HP Cube alumnis here to talk about Supercomputing Road to Quantum. Kirk, great to see you. Thanks for coming on. >>Thanks for having me guys. Great to be >>Here. So Paul and I were talking and we've been covering, you know, computing as we get into the large scale cloud now on premises compute has been one of those things that just never stops. No one ever, I never heard someone say, I wanna run my application or workload on slower, slower hardware or processor or horsepower. Computing continues to go, but this, we're at a step function. It feels like we're at a level where we're gonna unleash new, new creativity, new use cases. You've been kind of working on this for many, many years at hp, Hewlett Packard Labs, I remember the machine and all the predecessor r and d. Where are we right now from your standpoint, HPE standpoint? Where are you in the computing? It's as a service, everything's changing. What's your view? >>So I think, you know, you capture so well. You think of the capabilities that you create. You create these systems and you engineer these amazing products and then you think, whew, it doesn't get any better than that. And then you remind yourself as an engineer. But wait, actually it has to, right? It has to because we need to continuously provide that next generation of scientists and engineer and artists and leader with the, with the tools that can do more and do more frankly with less. Because while we want want to run the program slower, we sure do wanna run them for less energy. And figuring out how we accomplish all of those things, I think is, is really where it's gonna be fascinating. And, and it's also, we think about that, we think about that now, scale data center billion, billion operations per second, the new science, arts and engineering that we'll create. And yet it's also what's beyond what's beyond that data center. How do we hook it up to those fantastic scientific instruments that are capable to generate so much information? We need to understand how we couple all of those things together. So I agree, we are at, at an amazing opportunity to raise the aspirations of the next generation. At the same time we have to think about what's coming next in terms of the technology. Is the silicon the only answer for us to continue to advance? >>You know, one of the big conversations is like refactoring, replatforming, we have a booth behind us that's doing energy. You can build it in data centers for compute. There's all kinds of new things. Is there anything in the paradigm of computing and now on the road to quantum, which I know you're involved, I saw you have on LinkedIn, you have an open rec for that. What paradigm elements are changing that weren't in play a few years ago that you're looking at right now as you look at the 20 mile stair into quantum? >>So I think for us it's fascinating because we've had a tailwind at our backs my whole career, 33 years at hp. And what I could count on was transistors got at first they got cheaper, faster and they use less energy. And then, you know, that slowed down a little bit. Now they're still cheaper and faster. As we look in that and that Moore's law continues to flatten out of it, there has to be something better to do than, you know, yet another copy of the prior design opening up that diversity of approach. And whether that is the amazing wafer scale accelerators, we see these application specific silicon and then broadening out even farther next to the next to the silicon. Here's the analog computational accelerator here is now the, the emergence of a potential quantum accelerator. So seeing that diversity of approaches, but what we have to happen is we need to harness all of those efficiencies and yet we still have to realize that there are human beings that need to create the application. So how do we bridge, how do we accommodate the physical of, of new kinds of accelerator? How do we imagine the cyber physical connection to the, to the rest of the supercomputer? And then finally, how do we bridge that productivity gap? Especially not for people who like me who have been around for a long time, we wanna think about that next generation cuz they're the ones that need to solve the problems and write the code that will do it. >>You mentioned what exists beyond silicon. In fact, are you looking at different kinds of materials that computers in the future will be built upon? >>Oh absolutely. You think of when, when we, we look at the quantum, the quantum modalities then, you know, whether it is a trapped ion or a superconducting, a piece of silicon or it is a neutral ion. There's just no, there's about half a dozen of these novel systems because really what we're doing when we're using a a quantum mechanical computer, we're creating a tiny universe. We're putting a little bit of material in there and we're manipulating at, at the subatomic level, harnessing the power of of, of quantum physics. That's an incredible challenge. And it will take novel materials, novel capabilities that we aren't just used to seeing. Not many people have a helium supplier in their data center today, but some of them might tomorrow. And understanding again, how do we incorporate industrialize and then scale all of these technologies. >>I wanna talk Turkey about quantum because we've been talking for, for five years. We've heard a lot of hyperbole about quantum. We've seen some of your competitors announcing quantum computers in the cloud. I don't know who's using these, these computers, what kind of work they're being used, how much of the, how real is quantum today? How close are we to having workable true quantum computers and what can you point to any examples of how it's being, how that technology is being used in the >>Field? So it, it remains nascent. We'll put it that way. I think part of the challenge is we see this low level technology and of course it was, you know, professor Richard Fineman who first pointed us in this direction, you know, more than 30 years ago. And you know, I I I trust his judgment. Yes. You know that there's probably some there there especially for what he was doing, which is how do we understand and engineer systems at the quantum mechanical level. Well he said a quantum mechanical system's probably the way to go. So understanding that, but still part of the challenge we see is that people have been working on the low level technology and they're reaching up to wondering will I eventually have a problem that that I can solve? And the challenge is you can improve something every single day and if you don't know where the bar is, then you don't ever know if you'll be good enough. >>I think part of the approach that we like to understand, can we start with the problem, the thing that we actually want to solve and then figure out what is the bespoke combination of classical supercomputing, advanced AI accelerators, novel quantum quantum capabilities. Can we simulate and design that? And we think there's probably nothing better to do that than than an next to scale supercomputer. Yeah. Can we simulate and design that bespoke environment, create that digital twin of this environment and if we, we've simulated it, we've designed it, we can analyze it, see is it actually advantageous? Cuz if it's not, then we probably should go back to the drawing board. And then finally that then becomes the way in which we actually run the quantum mechanical system in this hybrid environment. >>So it's na and you guys are feeling your way through, you get some moonshot, you work backwards from use cases as a, as a more of a discovery navigational kind of mission piece. I get that. And Exoscale has been a great role for you guys. Congratulations. Has there been strides though in quantum this year? Can you point to what's been the, has the needle moved a little bit a lot or, I mean it's moving I guess to some, there's been some talk but we haven't really been able to put our finger on what's moving, like what need, where's the needle moved I >>Guess in quantum. And I think, I think that's part of the conversation that we need to have is how do we measure ourselves. I know at the World Economic Forum, quantum Development Network, we had one of our global future councils on the future of quantum computing. And I brought in a scene I EEE fellow Par Gini who, you know, created the international technology roadmap for semiconductors. And I said, Paulo, could you come in and and give us examples, how was the semiconductor community so effective not only at developing the technology but predicting the development of technology so that whether it's an individual deciding if they should change careers or it's a nation state deciding if they should spend a couple billion dollars, we have that tool to predict the rate of change and improvement. And so I think that's part of what we're hoping by participating will bring some of that road mapping skill and technology and understanding so we can make those better reasoned investments. >>Well it's also fun to see super computing this year. Look at the bigger picture, obviously software cloud natives running modern applications, infrastructure as code that's happening. You're starting to see the integration of, of environments almost like a global distributed operating system. That's the way I call it. Silicon and advancements have been a big part of what we see now. Merchant silicon, but also dpu are on the scene. So the role role of silicon is there. And also we have supply chain problems. So how, how do you look at that as a a, a chief architect of h Hewlett Packard Labs? Because not only you have to invent the future and dream it up, but you gotta deal with the realities and you get the realities are silicon's great, we need more of that quantums around the corner, but supply chain, how do you solve that? What's your thoughts and how do you, how, how is HPE looking at silicon innovation and, and supply chain? >>And so for us it, it is really understanding that partnership model and understanding and contributing. And so I will do things like I happen to be the, the systems and architectures chapter editor for the I eee International Roadmap for devices and systems, that community that wants to come together and provide that guidance. You know, so I'm all about telling the semiconductor and the post semiconductor community, okay, this is where we need to compute. I have a partner in the applications and benchmark that says, this is what we need to compute. And when you can predict in the future about where you need to compute, what you need to compute, you can have a much richer set of conversations because you described it so well. And I think our, our senior fellow Nick Dubey would, he's coined the term internet of workflows where, you know, you need to harness everything from the edge device all the way through the extra scale computer and beyond. And it's not just one sort of static thing. It is a very interesting fluid topology. I'll use this compute at the edge, I'll do this information in the cloud, I want to have this in my exoscale data center and I still need to provide the tool so that an individual who's making that decision can craft that work flow across all of those different resources. >>And those workflows, by the way, are complicated. Now you got services being turned on and off. Observability is a hot area. You got a lot more data in in cycle inflow. I mean a lot more action. >>And I think you just hit on another key point for us and part of our research at labs, I have, as part of my other assignments, I help draft our AI ethics global policies and principles and not only tell getting advice about, about how we should live our lives, it also became the basis for our AI research lab at Shewl Packard Labs because they saw, here's a challenge and here's something where I can't actually believe, maintain my ethical compliance. I need to have engineer new ways of, of achieving artificial intelligence. And so much of that comes back to governance over that data and how can we actually create those governance systems and and do that out in the open >>That's a can of worms. We're gonna do a whole segment on that one, >>On that >>Technology, on that one >>Piece I wanna ask you, I mean, where rubber meets the road is where you're putting your dollars. So you've talked a lot, a lot of, a lot of areas of, of progress right now, where are you putting your dollars right now at Hewlett Packard Labs? >>Yeah, so I think when I draw, when I draw my 2030 vision slide, you know, I, for me the first column is about heterogeneous, right? How do we bring all of these novel computational approaches to be able to demonstrate their effectiveness, their sustainability, and also the productivity that we can drive from, from, from them. So that's my first column. My section column is that edge to exoscale workflow that I need to be able to harness all of those computational and data resources. I need to be aware of the energy consequence of moving data, of doing computation and find all of that while still maintaining and solving for security and privacy. But the last thing, and, and that's one was a, one was a how one was aware. The last thing is a who, right? And is is how do we take that subject matter expert? I think of a, a young engineer starting their career at hpe. It'll be very different than my 33 years. And part of it, you know, they will be undaunted by any, any scale. They will be cloud natives, maybe they metaverse natives, they will demand to design an open cooperative environment. So for me it's thinking about that individual and how do I take those capabilities, heterogeneous edge to exito scale workflows and then make them productive. And for me, that's, that's where we were putting our emphasis on those three. When, where and >>Who. Yeah. And making it compatible for the next generation. We see the student cluster competition going on over there. This is the only show that we cover that we've been to that is from the dorm room to the boardroom and this cuz Supercomputing now is elevating up into that workflow, into integration, multiple environments, cloud, premise, edge, metaverse. This is like a whole nother world. >>And, and, but I think it's, it's the way that regardless of which human pursuit you're in, you know, everyone is going to be demand simulation and modeling ai, ML and massive data m l and massive data analytics that's gonna be at heart of, of everything. And that's what you see. That's what I love about coming here. This isn't just the way we're gonna do science. This is the way we're gonna do everything. >>We're gonna come by your booth, check it out. We've talked to some of the folks, hpe obviously HPE Discover this year, GreenLake with center stage, it's now consumption is a service for technology. Whole nother ballgame. Congratulations on, on all this. I would say the massive, I won't say pivot, but you know, a change >>It >>Is and how you guys >>Operate. And you know, it's funny sometimes you think about the, the pivot to as a services benefiting the customer, but as someone who has supported designs over decades, you know, that ability to to to operate and at peak efficiency, to always keep in perfect operating order and to continuously change while still meeting the customer expectations that actually allows us to deliver innovation to our customers faster than when we are delivering warranted individual packaged products. >>Kirk, thanks for coming on Paul. Great conversation here. You know, the road to Quantum's gonna be paved through computing supercomputing software integrated workflows from the dorm room to the boardroom to Cube, bringing all the action here at Supercomputing 22. I'm Jacque Forer with Paul Gillin. Thanks for watching. We'll be right back.