>> Hello, everyone. Welcome to theCUBE's coverage of Compute Engineered For Your Hybrid World, sponsored by HPE and Intel. I'm John Furrier, host of theCUBE. I'm pleased to be joined by Krista Satterthwaite, SVP and general manager for HPE Mainstream Compute, and Lisa Spelman, corporate vice president, and general manager of Intel Xeon Products, here to discuss the major announcement. Thanks for joining us today. Thanks for coming on theCUBE. >> Thanks for having us. >> Great to be here. >> Great to see you guys. And exciting announcement. Krista, Compute continues to evolve to meet the challenges of businesses. We're seeing more and more high performance, more Compute, I mean, it's getting more Compute every day. You guys officially announced this next generation of ProLiant Gen11s in November. Can you share and talk about what this means? >> Yeah, so first of all, thanks so much for having me. I'm really excited about this announcement. And yeah, in November we announced our HPE ProLiant NextGen, and it really was about one thing. It's about engineering Compute for customers' hybrid world. And we have three different design principles when we designed this generation. First is intuitive cloud operating experience, and that's with our HPE GreenLake for Compute Ops Management. And that's all about management that is simple, unified, and automated. So it's all about seeing everything from one council. So you have a customer that's using this, and they were so surprised at how much they could see, and they were excited because they had servers in multiple locations. This was a hotel, so they had servers everywhere, and they can now see all their different firmware levels. And with that type of visibility, they thought their planning was going to be much, much easier. And then when it comes to updates, they're much quicker and much easier, so it's an exciting thing, whether you have servers just in the data center, or you have them distributed, you could see and do more than you ever could before with HPE GreenLake for Compute Ops Management. So that's number one. Number two is trusted security by design. Now, when we launched our HPE ProLiant Gen10 servers years ago, we launched groundbreaking innovative security features, and we haven't stopped, we've continued to enhance that every since then. And this generation's no exception. So we have new innovations around security. Security is a huge focus area for us, and so we're excited about delivering those. And then lastly, performance for every workload. We have a huge increase in performance with HPE ProLiant Gen11, and we have customers that are clamoring for this additional performance right now. And what's great about this is that, it doesn't matter where the bottleneck is, whether it's CPU, memory or IO, we have advancements across the board that are going to make real differences in what customers are going to be able to get out of their workloads. And then we have customers that are trying to build headroom in. So even if they don't need a today, what they put in their environment today, they know needs to last and need to be built for the future. >> That's awesome. Thanks for the recap. And that's great news for folks looking to power those workloads, more and more optimizations needed. I got to ask though, how is what you guys are announcing today, meeting these customer needs for the future, and what are your customers looking for and what are HPE and Intel announcing today? >> Yeah, so customers are doing more than ever before with their servers. So they're really pushing things to the max. I'll give you an example. There's a retail customer that is waiting to get their hands on our ProLiant Gen11 servers, because they want to do video streaming in every one of their retail stores and what they're building, when they're building what they need, we started talking to 'em about what their needs were today, and they were like, "Forget about what my needs are today. We're buying for headroom. We don't want to touch these servers for a while." So they're maxing things out, because they know the needs are coming. And so what you'll see with this generation is that we've built all of that in so that customers can deploy with confidence and know they have the headroom for all the things they want to do. The applications that we see and what people are trying to do with their servers is light years different than the last big announcement we had, which was our ProLiant Gen10 servers. People are trying to do more than ever before and they're trying to do that at the Edge as well as as the data center. So I'll tell you a little bit about the servers we have. So in partnership with Intel, we're really excited to announce a new batch of servers. And these servers feature the 4th Gen Intel Xeon scalable processors, bringing a lot more performance and efficiency. And I'll talk about the servers, one, the first one is a HPE ProLiant DL320 Gen11. Now, I told you about that retail customer that's trying to do video streaming in their stores. This is the server they were looking at. This server is a new server, we didn't have a Gen10 or a Gen10+ version of the server. This is a new server and it's optimized for Edge use cases. It's a rack-based server and it's very, very flexible. So different types of storage, different types of GPU configurations, really designed to take care of many, many use cases at the Edge and doing more at the Edge than ever before. So I mentioned video streaming, but also VDI and analytics at the Edge. The next two servers are some of our most popular servers, our HPE ProLiant DL360 Gen11, and that's our density-optimized server for enterprise. And that is getting an upgrade across the board as well, big, big improvements in terms of performance, and expansion. And for those customers that need even more expansion when it comes to, let's say, storage or accelerators then the DL 380 Gen11 is a server that's new as well. And that's really for folks that need more expandability than the DL360, which is a one use server. And then lastly, our ML350, which is a tower server. These tower servers are typically used at remote sites, branch offices and this particular server holds a world record for energy efficiency for tower servers. So those are some of the servers we have today that we're announcing. I also want to talk a little bit about our Cray portfolio. So we're announcing two new servers with our HPE Cray portfolio. And what's great about this is that these servers make super computing more accessible to more enterprise customers. These servers are going to be smaller, they're going to come in at lower price points, and deliver tremendous energy efficiency. So these are the Cray XD servers, and there's more servers to come, but these are the ones that we're announcing with this first iteration. >> Great stuff. I can talk about servers all day long, I love server innovation. It's been following for many, many years, and you guys know. Lisa, we'll bring you in. Servers have been powered by Intel Xeon, we've been talking a lot about the scalable processors. This is your 4th Gen, they're in Gen11 and you're at 4th Gen. Krista mentioned this generation's about Security Edge, which is essentially becoming like a data center model now, the Edges are exploding. What are some of the design principles that went into the 4th Gen this time around the scalable processor? Can you share the Intel role here? >> Sure. I love what Krista said about headroom. If there's anything we've learned in these past few years, it's that you can plan for today, and you can even plan for tomorrow, but your tomorrow might look a lot different than what you thought it was going to. So to meet these business challenges, as we think about the underlying processor that powers all that amazing server lineup that Krista just went through, we are really looking at delivering that increased performance, the power efficient compute and then strong security. And of course, attention to the overall operating cost of the customer environment. Intel's focused on a very workload-first approach to solving our customers' real problems. So this is the applications that they're running every day to drive their digital transformation, and we really like to focus our innovation, and leadership for those highest value, and also the highest growth workloads. Some of those that we've uniquely focused on in 4th Gen Xeon, our artificial intelligence, high performance computing, network, storage, and as well as the deployments, like you were mentioning, ranging from the cloud all the way out to the Edge. And those are all satisfied by 4th Gen Xeon scalable. So our strategy for architecting is based off of all of that. And in addition to doing things like adding core count, improving the platform, updating the memory and the IO, all those standard things that you do, we've invested deeply in delivering the industry's CPU with the most built-in accelerators. And I'll just give an example, in artificial intelligence with built-in AMX acceleration, plus the framework optimizations, customers can see a 10X performance improvement gen over gen, that's on both training and inference. So it further cements Xeon as the world's foundation for inference, and it now delivers performance equivalent of a modern GPU, but all within your CPU. The flexibility that, that opens up for customers is tremendous and it's so many new ways to utilize their infrastructure. And like Krista said, I just want to say that, that best-in-class security, and security solutions are an absolute requirement. We believe that starts at the hardware level, and we continue to invest in our security features with that full ecosystem support so that our customers, like HPE, can deliver that full stacked solution to really deliver on that promise. >> I love that scalable processor messaging too around the silicon and all those advanced features, the accelerators. AI's certainly seeing a lot of that in demand now. Krista, similar question to you on your end. How do you guys look at these, your core design principles around the ProLiant Gen11, and how that helps solve the challenges for your customers that are living in this hybrid world today? >> Yeah, so we see how fast things are changing and we kept that in mind when we decided to design this generation. We talked all already about distributed environments. We see the intensity of the requirements that are at the Edge, and that's part of what we're trying to address with the new platform that I mentioned. It's also part of what we're trying to address with our management, making sure that people can manage no matter where a server is and get a great experience. The other thing we're realizing when it comes to what's happening is customers are looking at how they operate. Many want to buy as a service and with HPE GreenLake, we see that becoming more and more popular. With HPE GreenLake, we can offer that to customers, which is really helpful, especially when they're trying to get new technology like this. Sometimes they don't have it in the budget. With something like HP GreenLake, there's no upfront costs so they can enjoy this technology without having to come up with a big capital outlay for it. So that's great. Another one is around, I liked what Lisa said about security starting at the hardware. And that's exactly, the foundation has to be secure, or you're starting at the wrong place. So that's also something that we feel like we've advanced this time around. This secure root of trust that we started in Gen10, we've extended that to additional partners, so we're excited about that as well. >> That's great, Krista. We're seeing and hearing a lot about customers challenges at the Edge. Lisa, I want to bring you back in on this one. What are the needs that you see at the Edge from an Intel perspective? How is Intel addressing the Edge? >> Yeah, thanks, John. You know, one of the best things about Xeon is that it can span workloads and environments all the way from the Edge back to the core data center all within the same software environment. Customers really love that portability. For the Edge, we have seen an explosion of use cases coming from all industries and I think Krista would say the same. Where we're focused on delivering is that performant-enough compute that can fit into a constrained environment, and those constraints can be physical space, they can be the thermal environment. The Network Edge has been a big focus for us. Not only adding features and integrating acceleration, but investing deeply in that software environment so that more and more critical applications can be ported to Xeon and HPE industry standard servers versus requiring expensive, proprietary systems that were quite frankly not designed for this explosion of use cases that we're seeing. Across a variety of Edge to cloud use cases, we have identified ways to provide step function improvements in both performance and that power efficiency. For example, in this generation, we're delivering an up to 2.9X average improvement in performance per watt versus not using accelerators, and up to 70 watt power savings per CPU opportunity with some unique power management features, and improve total cost of ownership, and just overall power- >> What's the closing thoughts? What should people take away from this announcement around scalable processors, 4th Gen Intel, and then Gen11 ProLiant? What's the walkaway? What's the main super thought here? >> So I can go first. I think the main thought is that, obviously, we have partnered with Intel for many, many years. We continue to partner this generation with years in the making. In fact, we've been working on this for years, so we're both very excited that it's finally here. But we're laser focused on making sure that customers get the most out of their workloads, the most out of their infrastructure, and that they can meet those challenges that people are throwing at 'em. I think IT is under more pressure than ever before and the demands are there. They're critical to the business success with digital transformation and our job is to make sure they have everything they need, and they could do and meet the business needs as they come at 'em. >> Lisa, your thoughts on this reflection point we're in right now? >> Well, I agree with everything that Krista said. It's just a really exciting time right now. There's a ton of challenges in front of us, but the opportunity to bring technology solutions to our customers' digital transformation is tremendous right now. I think I would also like our customers to take away that between the work that Intel and HPE have done together for generations, they have a community that they can trust. We are committed to delivering customer-led solutions that do solve these business transformation challenges that we know are in front of everyone, and we're pretty excited for this launch. >> Yeah, I'm super enthusiastic right now. I think you guys are on the right track. This title Compute Engineered for Hybrid World really kind of highlights the word, "Engineered." You're starting to see this distributed computing architecture take shape with the Edge. Cloud on-premise computing is everywhere. This is real relevant to your customers, and it's a great announcement. Thanks for taking the time and joining us today. >> Thank you. >> Yeah, thank you. >> This is the first episode of theCUBE's coverage of Compute Engineered For Your Hybrid World. Please continue to check out thecube.net, our site, for the future episodes where we'll discuss how to build high performance AI applications, transforming compute management experiences, and accelerating VDI at the Edge. Also, to learn more about the new HPE ProLiant servers with the 4th Gen Intel Xeon processors, you can go to hpe.com. And check out the URL below, click on it. I'm John Furrier at theCUBE. You're watching theCUBE, the leader in high tech, enterprise coverage. (bright music)

(upbeat music) >> Welcome back. We're here at Supercomputing 22 in Dallas. My name's Paul Gill, I'm your host. With me, Dave Nicholson, my co-host. And one thing that struck me about this conference arriving here, was the number of universities that are exhibiting here. I mean, big, big exhibits from universities. Never seen that at a conference before. And one of those universities is Indiana University. Our two guests, Dave Jent, who's the AVP of Networks at Indiana University, Aaron Neal, Deputy CIO at Indiana University. Welcome, thanks for joining us. >> Thank you for having us. >> Thank you. >> I've always thought that the CIO job at a university has got to be the toughest CIO job there is, because you're managing this sprawling network, people are doing all kinds of different things on it. You've got to secure it. You've got to make it performant. And it just seems to be a big challenge. Talk about the network at Indiana University and what you have done particularly since the pandemic, how that has affected the architecture of your network. And what you do to maintain the levels of performance and security that you need. >> On the network side one of the things we've done is, kept in close contact with what the incoming students are looking for. It's a different environment than it was then 10 years ago when a student would come, maybe they had a phone, maybe they had one laptop. Today they're coming with multiple phones, multiple laptops, gaming devices. And the expectation that they have to come on a campus and plug all that stuff in causes lots of problems for us, in managing just the security aspect of it, the capacity, the IP space required to manage six, seven devices per student when you have 35,000 students on campus, has always been a challenge. And keeping ahead of that knowing what students are going to come in with, has been interesting. During the pandemic the campus was closed for a bit of time. What we found was our biggest challenge was keeping up with the number of people who wanted to VPN to campus. We had to buy additional VPN licenses so they could do their work, authenticate to the network. We doubled, maybe even tripled our our VPN license count. And that has settled down now that we're back on campus. But again, they came back with a vengeance. More gaming devices, more things to be connected, and into an environment that was a couple years old, that we hadn't done much with. We had gone through a pretty good size network deployment of new hardware to try to get ready for them. And it's worked well, but it's always challenging to keep up with students. >> Aaron, I want to ask you about security because that really is one of your key areas of focus. And you're collaborating with counties, local municipalities, as well as other educational institutions. How's your security strategy evolving in light of some of the vulnerabilities of VPNs that became obvious during the pandemic, and this kind of perfusion of new devices that that Dave was talking about? >> Yeah, so one of the things that we we did several years ago was establish what we call OmniSOC, which is a shared security operations center in collaboration with other institutions as well as research centers across the United States and in Indiana. And really what that is, is we took the lessons that we've learned and the capabilities that we've had within the institution and looked to partner with those key institutions to bring that data in-house, utilize our staff such that we can look for security threats and share that information across the the other institutions so that we can give each of those areas a heads up and work with those institutions to address any kind of vulnerabilities that might be out there. One of the other things that you mentioned is, we're partnering with Purdue in the Indiana Office of Technology on a grant to actually work with municipalities, county governments, to really assess their posture as it relates to security in those areas. It's a great opportunity for us to work together as institutions as well as work with the state in general to increase our posture as it relates to security. >> Dave, what brings IU to Supercomputing 2022? >> We've been here for a long time. And I think one of the things that we're always interested in is, what's next? What's new? There's so many, there's network vendors, software vendors, hardware vendors, high performance computing suppliers. What is out there that we're interested in? IU runs a large Cray system in Indiana called Big Red 200. And with any system you procure it, you get it running, you operate it, and your next goal is to upgrade it. And what's out there that we might be interested? That I think why we come to IU. We also like to showcase what we do at IU. If you come by the booth you'll see the OmniSOC, there's some video on that. The GlobalNOC, which I manage, which supports a lot of the RNE institutions in the country. We talk about that. Being able to have a place for people to come and see us. If you stand by the booth long enough people come and find you, and want to talk about a project they have, or a collaboration they'd like to partner with. We had a guy come by a while ago wanting a job. Those are all good things having a big booth can do for you. >> Well, so on that subject, in each of your areas of expertise and your purview are you kind of interleaved with the academic side of things on campus? Do you include students? I mean, I would think it would be a great source of cheap labor for you at least. Or is there kind of a wall between what you guys are responsible for and what students? >> Absolutely we try to support faculty and students as much as we can. And just to go back a little bit on the OmniSOC discussion. One of the things that we provide is internships for each of the universities that we work with. They have to sponsor at least three students every year and make that financial commitment. We bring them on site for three weeks. They learn us alongside the other analysts, information security analysts and work in a real world environment and gain those skills to be able to go back to their institutions and do an additional work there. So it's a great program for us to work with students. I think the other thing that we do is we provide obviously the infrastructure that enable our faculty members to do the research that they need to do. Whether that's through Big Red 200, our Supercomputer or just kind of the everyday infrastructure that allows them to do what they need to do. We have an environment on premise called our Intelligent Infrastructure, that we provide managed access to hardware and storage resources in a way that we know it's secure and they can utilize that environment to do virtually anything that they need in a server environment. >> Dave, I want to get back to the GigaPOP, which you mentioned earlier you're the managing director of the Indiana GigaPOP. What exactly is it? >> Well, the GigaPOP and there are a number of GigaPOP around the country. It was really the aggregation facility for Indiana and all of the universities in Indiana to connect to outside resources. GigaPOP has connections to internet too, the commodity internet, Esnet, the Big Ten or the BTAA a network in Chicago. It's a way for all universities in Indiana to connect to a single source to allow them to connect nationally to research organizations. >> And what are the benefits of having this collaboration of university. >> If you could think of a researcher at Indiana wants to do something with a researcher in Wisconsin, they both connect to their research networks in Wisconsin and Indiana, and they have essentially direct connection. There's no commodity internet, there's no throttling of of capacity. Both networks and the interconnects because we use internet too, are essentially UNT throttled access for the researchers to do anything they need to do. It's secure, it's fast, easy to use, in fact, so easy they don't even know that they're using it. It just we manage the networks and organize the networks in a way configure them that's the path of least resistance and that's the path traffic will take. And that's nationally. There are lots of these that are interconnected in various ways. I do want to get back to the labor point, just for a moment. (laughs) Because... >> You're here to claim you're not violating any labor laws. Is that what you're going to be? >> I'm here to hopefully hire, get more people to be interested to coming to IU. >> Stop by the booth. >> It's a great place to work. >> Exactly. >> We hire lots of interns and in the network space hiring really experienced network engineers, really hard to do, hard to attract people. And these days when you can work from anywhere, you don't have to be any place to work for anybody. We try to attract as many students as we can. And really we're exposing 'em to an environment that exists in very few places. Tens of thousands of wireless access points, big fast networks, interconnections and national international networks. We support the Noah network which supports satellite systems and secure traffic. It really is a very unique experience and you can come to IU, spend lots of years there and never see the same thing twice. We think we have an environment that's really a good way for people to come out of college, graduate school, work for some number of years and hopefully stay at IU, but if not, leave and get a good job and talk well about IU. In fact, the wireless network today here at SC was installed and is managed by a person who manages our campus network wireless, James Dickerson. That's the kind of opportunity we can provide people at IU. >> Aaron, I'd like to ask, you hear a lot about everything moving to the cloud these days, but in the HPC world I don't think that move is happening as quickly as it is in some areas. In fact, there's a good argument some workloads should never move to the cloud. You're having to balance these decisions. Where are you on the thinking of what belongs in the data center and what belongs in the cloud? >> I think our approach has really been specific to what the needs are. As an institution, we've not pushed all our chips in on the cloud, whether it be for high performance computing or otherwise. It's really looking at what the specific need is and addressing it with the proper solution. We made an investment several years ago in a data center internally, and we're leveraging that through the intelligent infrastructure that I spoke about. But really it's addressing what the specific need is and finding the specific solution, rather than going all in in one direction or another. I dunno if Jet Stream is something that you would like to bring up as well. >> By having our own data center and having our own facilities we're able to compete for NSF grants and work on projects that provide shared resources for the research community. Just dream is a project that does that. Without a data center and without the ability to work on large projects, we don't have any of that. If you don't have that then you're dependent on someone else. We like to say that, what we are proud of is the people come to IU and ask us if they can partner on our projects. Without a data center and those resources we are the ones who have to go out and say can we partner on your project? We'd like to be the leaders of that in that space. >> I wanted to kind of double click on something you mentioned. Couple of things. Historically IU has been I'm sure closely associated with Chicago. You think of what are students thinking of doing when they graduate? Maybe they're going to go home, but the sort of center of gravity it's like Chicago. You mentioned talking about, especially post pandemic, the idea that you can live anywhere. Not everybody wants to live in Manhattan or Santa Clara. And of course, technology over decades has given us the ability to do things remotely and IU is plugged into the globe, doesn't matter where you are. But have you seen either during or post pandemic 'cause we're really in the early stages of this. Are you seeing that? Are you seeing people say, Hey, thinking about their family, where do I want to live? Where do I want to raise my family? I'm in academia and no, I don't want to live in Manhattan. Hey, we can go to IU and we're plugged into the globe. And then students in California we see this, there's some schools on the central coast where people loved living there when they were in college but there was no economic opportunity there. Are you seeing a shift, are basically houses in Bloomington becoming unaffordable because people are saying, you know what, I'm going to stay here. What does that look like? >> I mean, for our group there are a lot of people who do work from home, have chosen to stay in Bloomington. We have had some people who for various reasons want to leave. We want to retain them, so we allow them to work remotely. And that has turned into a tool for recruiting. The kid that graduates from Caltech. Doesn't want to stay in Caltech in California, we have an opportunity now he can move to wherever between here and there and we can hire him do work. We love to have people come to Indiana. We think it is a unique experience, Bloomington, Indianapolis are great places. But I think the reality is, we're not going to get everybody to come live, be a Hoosier, how do we get them to come and work at IU? In some ways disappointing when we don't have buildings full of people, but 40 paying Zoom or teams window, not kind the same thing. But I think this is what we're going to have to figure out, how do we make this kind of environment work. >> Last question here, give you a chance to put in a plug for Indiana University. For those those data scientists those researchers who may be open to working somewhere else, why would they come to Indiana University? What's different about what you do from what every other academic institution does, Aaron? >> Yeah, I think a lot of what we just talked about today in terms of from a network's perspective, that were plugged in globally. I think if you look beyond the networks I think there are tremendous opportunities for folks to come to Bloomington and experience some bleeding edge technology and to work with some very talented people. I've been amazed, I've been at IU for 20 years and as I look at our peers across higher ed, well, I don't want to say they're not doing as well I do want brag at how well we're doing in terms of organizationally addressing things like security in a centralized way that really puts us in a better position. We're just doing a lot of things that I think some of our peers are catching up to and have been catching up to over the last 10, 12 years. >> And I think to sure scale of IU goes unnoticed at times. IU has the largest medical school in the country. One of the largest nursing schools in the country. And people just kind of overlook some of that. Maybe we need to do a better job of talking about it. But for those who are aware there are a lot of opportunities in life sciences, healthcare, the social sciences. IU has the largest logistics program in the world. We teach more languages than anybody else in the world. The varying kinds of things you can get involved with at IU including networks, I think pretty unparalleled. >> Well, making the case for high performance computing in the Hoosier State. Aaron, Dave, thanks very much for joining you making a great case. >> Thank you. >> Thank you. >> We'll be back right after this short message. This is theCUBE. (upbeat music)

(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)

>> Announcer: theCube presents HPE Discover 2022, brought to you by HPE. >> Hey, everyone. Welcome back to Las Vegas. This is Lisa Martin with Dave Vellante live at HPE Discover '22. Dave, it's great to be here. This is the first Discover in three years and we're here with about 7,000 of our closest friends. >> Yeah. You know, I tweeted out this, I think I've been to 14 Discovers between the U.S. and Europe, and I've never seen a Discover with so much energy. People are not only psyched to get back together, that's for sure, but I think HPE's got a little spring in its step and it's feeling more confident than maybe some of the past Discovers that I've been to. >> I think so, too. I think there's definitely a spring in the step and we're going to be unpacking some of that spring next with one of our alumni who joins us, Keith White's here, the executive vice president and general manager of GreenLake Cloud Services. Welcome back. >> Great. You all thanks for having me. It's fantastic that you're here and you're right, the energy is crazy at this show. It's been a lot of pent up demand, but I think what you heard from Antonio today is our strategy's changing dramatically and it's really embracing our customers and our partners. So it's great. >> Embracing the customers and the partners, the ecosystem expansion is so critical, especially the last couple of years with the acceleration of digital transformation. So much challenge in every industry, but lots of momentum on the GreenLake side, I was looking at the Q2 numbers, triple digit growth in orders, 65,000 customers over 70 services, eight new services announced just this morning. Talk to us about the momentum of GreenLake. >> The momentum's been fantastic. I mean, I'll tell you, the fact that customers are really now reaccelerating their digital transformation, you probably heard a lot, but there was a delay as we went through the pandemic. So now it's reaccelerating, but everyone's going to a hybrid, multi-cloud environment. Data is the new currency. And obviously, everyone's trying to push out to the Edge and GreenLake is that edge to cloud platform. So we're just seeing tons of momentum, not just from the customers, but partners, we've enabled the platform so partners can plug into it and offer their solutions to our customers as well. So it's exciting and it's been fun to see the momentum from an order standpoint, but one of the big numbers that you may not be aware of is we have over a 96% retention rate. So once a customer's on GreenLake, they stay on it because they're seeing the value, which has been fantastic. >> The value is absolutely critically important. We saw three great big name customers. The Home Depot was on stage this morning, Oak Ridge National Laboratory was as well, Evil Geniuses. So the momentum in the enterprise is clearly present. >> Yeah. It is. And we're hearing it from a lot of customers. And I think you guys talk a lot about, hey, there's the cloud, data and Edge, these big mega trends that are happening out there. And you look at a company like Barclays, they're actually reinventing their entire private cloud infrastructure, running over a hundred thousand workloads on HPE GreenLake. Or you look at a company like Zenseact, who's basically they do autonomous driving software. So they're doing massive parallel computing capabilities. They're pulling in hundreds of petabytes of data to then make driving safer and so you're seeing it on the data front. And then on the Edge, you look at anyone like a Patrick Terminal, for example. They run a whole terminal shipyard. They're getting data in from exporters, importers, regulators, the works and they have to real-time, analyze that data and say, where should this thing go? Especially with today's supply chain challenges, they have to be so efficient, that it's just fantastic. >> It was interesting to hear Fidelma, Keith, this morning on stage. It was the first time I'd really seen real clarity on the platform itself and that it's obviously her job is, okay, here's the platform, now, you guys got to go build on top of it. Both inside of HPE, but also externally, so your ecosystem partners. So, you mentioned the financial services companies like Barclays. We see those companies moving into the digital world by offering some of their services in building their own clouds. >> Keith: That's right. >> What's your vision for GreenLake in terms of being that platform, to assist them in doing that and the data component there? >> I think that was one of the most exciting things about not just showcasing the platform, but also the announcement of our private cloud enterprise, Cloud Service. Because in essence, what you're doing is you're creating that framework for what most companies are doing, which is they're becoming cloud service providers for their internal business units. And they're having to do showback type scenarios, chargeback type scenarios, deliver cloud services and solutions inside the organization so that open platform, you're spot on. For our ecosystem, it's fantastic, but for our customers, they get to leverage it as well for their own internal IT work that's happening. >> So you talk about hybrid cloud, you talk about private cloud, what's your vision? You know, we use this term Supercloud. This in a layer that goes across clouds. What's your thought about that? Because you have an advantage at the Edge with Aruba. Everybody talks about the Edge, but they talk about it more in the context of near Edge. >> That's right. >> We talked to Verizon and they're going far Edge, you guys are participating in that, as well as some of your partners in Red Hat and others. What's your vision for that? What I call Supercloud, is that part of the strategy? Is that more longer term or you think that's pipe dream by Dave? >> No, I think it's really thoughtful, Dave, 'cause it has to be part of the strategy. What I hear, so for example, Ford's a great example. They run Azure, AWS, and then they made a big deal with Google cloud for their internal cars and they run HPE GreenLake. So they're saying, hey, we got four clouds. How do we sort of disaggregate the usage of that? And Chris Lund, who is the VP of information technology at Liberty Mutual Insurance, he talked about it today, where he said, hey, I can deliver these services to my business unit. And they don't know, am I running on the public cloud? Am I running on our HPE GreenLake cloud? Like it doesn't matter to the end user, we've simplified that so much. So I think your Supercloud idea is super thoughtful, not to use the super term too much, that I'm super excited about because it's really clear of what our customers are trying to accomplish, which it's not about the cloud, it's about the solution and the business outcome that gets to work. >> Well, and I think it is different. I mean, it's not like the last 10 years where it was like, hey, I got my stuff to work on the different clouds and I'm replicating as much as I can, the cloud experience on-prem. I think you guys are there now and then to us, the next layer is that ecosystem enablement. So how do you see the ecosystem evolving and what role does Green Lake play there? >> Yeah. This has been really exciting. We had Tarkan Maner who runs Nutanix and Karl Strohmeyer from Equinix on stage with us as well. And what's happening with the ecosystem is, I used to say, one plus one has to equal three for our customers. So when you bring these together, it has to be that scenario, but we are joking that one plus one plus one equals five now because everything has a partner component to it. It's not about the platform, it's not about the specific cloud service, it's actually about the solution that gets delivered. And that's done with an ISV, it's done with a Colo, it's done even with the Hyperscalers. We have Azure Stack HCI as a fully integrated solution. It happens with managed service providers, delivering managed services out to their folks as well. So that platform being fully partner enabled and that ecosystem being able to take advantage of that, and so we have to jointly go to market to our customers for their business needs, their business outcomes. >> Some of the expansion of the ecosystem. we just had Red Hat on in the last hour talking about- >> We're so excited to partner with them. >> Right, what's going on there with OpenShift and Ansible and Rel, but talk about the customer influence in terms of the expansion of the ecosystem. We know we've got to meet customers where they are, they're driving it, but we know that HPE has a big presence in the enterprise and some pretty big customer names. How are they from a demand perspective? >> Well, this is where I think the uniqueness of GreenLake has really changed HPE's approach with our customers. Like in all fairness, we used to be a vendor that provided hardware components for, and we talked a lot about hardware costs and blah, blah, blah. Now, we're actually a partner with those customers. What's the business outcome you're requiring? What's the SLA that we offer you for what you're trying to accomplish? And to do that, we have to have it done with partners. And so even on the storage front, Qumulo or Cohesity. On the backup and recovery disaster recovery, yes, we have our own products, but we also partner with great companies like Veeam because it's customer choice, it's an open platform. And the Red Hat announcement is just fantastic. Because, hey, from a container platform standpoint, OpenShift provides 5,000 plus customers, 90% of the fortune 500 that they engage with, with that opportunity to take GreenLake with OpenShift and implement that container capabilities on-prem. So it's fantastic. >> We were talking after the keynote, Keith Townsend came on, myself and Lisa. And he was like, okay, what about startups? 'Cause that's kind of a hallmark of cloud. And we felt like, okay, startups are not the ideal customer profile necessarily for HPE. Although we saw Evil Geniuses up on stage, but I threw out and I'd love to get your thoughts on this that within companies, incumbents, you have entrepreneurs, they're trying to build their own clouds or Superclouds as I use the term, is that really the target for the developer audience? We've talked a lot about OpenShift with their other platforms, who says as a partner- >> We just announced another extension with Rancher and- >> Yeah. I saw that. And you have to have optionality for developers. Is that the way we should think about the target audience from a developer standpoint? >> I think it will be as we go forward. And so what Fidelma presented on stage was the new developer platform, because we have come to realize, we have to engage with the developers. They're the ones building the apps. They're the ones that are delivering the solutions for the most part. So yeah, I think at the enterprise space, we have a really strong capability. I think when you get into the sort of mid-market SMB standpoint, what we're doing is we're going directly to the managed service and cloud service providers and directly to our Disty and VARS to have them build solutions on top of GreenLake, powered by GreenLake, to then deliver to their customers because that's what the customer wants. I think on the developer side of the house, we have to speak their language, we have to provide their capabilities because they're going to start articulating apps that are going to use both the public cloud and our on-prem capabilities with GreenLake. And so that's got to work very well. And so you've heard us talk about API based and all of that sort of scenario. So it's an exciting time for us, again, moving HPE strategy into something very different than where we were before. >> Well, Keith, that speaks to ecosystem. So I don't know if you were at Microsoft, when the sweaty Steve Ballmer was working with the developers, developers. That's about ecosystem, ecosystem, ecosystem. I don't expect we're going to see Antonio replicating that. But that really is the sort of what you just described is the ecosystem developing on top of GreenLake. That's critical. >> Yeah. And this is one of the things I learned. So, being at Microsoft for as long as I was and leading the Azure business from a commercial standpoint, it was all about the partner and I mean, in all fairness, almost every solution that gets delivered has some sort of partner component to it. Might be an ISV app, might be a managed service, might be in a Colo, might be with our hybrid cloud, with our Hyperscalers, but everything has a partner component to it. And so one of the things I learned with Azure is, you have to sell through and with your ecosystem and go to that customer with a joint solution. And that's where it becomes so impactful and so powerful for what our customers are trying to accomplish. >> When we think about the data gravity and the value of data that put massive potential that it has, even Antonio talked about it this morning, being data rich but insights poor for a long time. >> Yeah. >> Every company in today's day and age has to be a data company to be competitive, there's no more option for that. How does GreenLake empower companies? GreenLake and its ecosystem empower companies to really live being data companies so that they can meet their customers where they are. >> I think it's a really great point because like we said, data's the new currency. Data's the new gold that's out there and people have to get their arms around their data estate. So then they can make these business decisions, these business insights and garner that. And Dave, you mentioned earlier, the Edge is bringing a ton of new data in, and my Zenseact example is a good one. But with GreenLake, you now have a platform that can do data and data management and really sort of establish and secure the data for you. There's no data latency, there's no data egress charges. And which is what we typically run into with the public cloud. But we also support a wide range of databases, open source, as well as the commercial ones, the sequels and those types of scenarios. But what really comes to life is when you have to do analytics on that and you're doing AI and machine learning. And this is one of the benefits I think that people don't realize with HPE is, the investments we've made with Cray, for example, we have and you saw on stage today, the largest supercomputer in the world. That depth that we have as a company, that then comes down into AI and analytics for what we can do with high performance compute, data simulations, data modeling, analytics, like that is something that we, as a company, have really deep, deep capabilities on. So it's exciting to see what we can bring to customers all for that spectrum of data. >> I was excited to see Frontier, they actually achieve, we hosted an event, co-produced event with HPE during the pandemic, Exascale day. >> Yeah. >> But we weren't quite at Exascale, we were like right on the cusp. So to see it actually break through was awesome. So HPC is clearly a differentiator for Hewlett Packard Enterprise. And you talk about the egress. What are some of the other differentiators? Why should people choose GreenLake? >> Well, I think the biggest thing is, that it's truly is a edge to cloud platform. And so you talk about Aruba and our capabilities with a network attached and network as a service capabilities, like that's fairly unique. You don't see that with the other companies. You mentioned earlier to me that compute capabilities that we've had as a company and the storage capabilities. But what's interesting now is that we're sort of taking all of that expertise and we're actually starting to deliver these cloud services that you saw on stage, private cloud, AI and machine learning, high performance computing, VDI, SAP. And now we're actually getting into these industry solutions. So we talked last year about electronic medical records, this year, we've talked about 5g. Now, we're talking about customer loyalty applications. So we're really trying to move from these sort of baseline capabilities and yes, containers and VMs and bare metal, all that stuff is important, but what's really important is the services that you run on top of that, 'cause that's the outcomes that our customers are looking at. >> Should we expect you to be accelerating? I mean, look at what you did with Azure. You look at what AWS does in terms of the feature acceleration. Should we expect HPE to replicate? Maybe not to that scale, but in a similar cadence, we're starting to see that. Should we expect that actually to go faster? >> I think you couched it really well because it's not as much about the quantity, but the quality and the uses. And so what we've been trying to do is say, hey, what is our swim lane? What is our sweet spot? Where do we have a superpower? And where are the areas that we have that superpower and how can we bring those solutions to our customers? 'Cause I think, sometimes, you get over your skis a bit, trying to do too much, or people get caught up in the big numbers, versus the, hey, what's the real meat behind it. What's the tangible outcome that we can deliver to customers? And we see just a massive TAM. I want to say my last analysis was around $42 billion in the next three years, TAM and the Azure service on-prem space. And so we think that there's nothing but upside with the core set of workloads, the core set of solutions and the cloud services that we bring. So yeah, we'll continue to innovate, absolutely, amen, but we're not in a, hey we got to get to 250 this and 300 that, we want to keep it as focused as we can. >> Well, the vast majority of the revenue in the public cloud is still compute. I mean, not withstanding, Microsoft obviously does a lot in SaaS, but I'm talking about the infrastructure and service. Still, well, I would say over 50%. And so there's a lot of the services that don't make any revenue and there's that long tail, if I hear your strategy, you're not necessarily going after that. You're focusing on the quality of those high value services and let the ecosystem sort of bring in the rest. >> This is where I think the, I mean, I love that you guys are asking me about the ecosystem because this is where their sweet spot is. They're the experts on hyper-converged or databases, a service or VDI, or even with SAP, like they're the experts on that piece of it. So we're enabling that together to our customers. And so I don't want to give you the impression that we're not going to innovate. Amen. We absolutely are, but we want to keep it within that, that again, our swim lane, where we can really add true value based on our expertise and our capabilities so that we can confidently go to customers and say, hey, this is a solution that's going to deliver this business value or this capability for you. >> The partners might be more comfortable with that than, we only have one eye sleep with one eye open in the public cloud, like, okay, what are they going to, which value of mine are they grab next? >> You're spot on. And again, this is where I think, the power of what an Edge to cloud platform like HPE GreenLake can do for our customers, because it is that sort of, I mentioned it, one plus one equals three kind of scenario for our customers so. >> So we can leave your customers, last question, Keith. I know we're only on day one of the main summit, the partner growth summit was yesterday. What's the feedback been from the customers and the ecosystem in terms of validating the direction that HPE is going? >> Well, I think the fantastic thing has been to hear from our customers. So I mentioned in my keynote recently, we had Liberty Mutual and we had Texas Children's Hospital, and they're implementing HPE GreenLake in a variety of different ways, from a private cloud standpoint to a data center consolidation. They're seeing sustainability goals happen on top of that. They're seeing us take on management for them so they can take their limited resources and go focus them on innovation and value added scenarios. So the flexibility and cost that we're providing, and it's just fantastic to hear this come to life in a real customer scenario because what Texas Children is trying to do is improve patient care for women and children like who can argue with that. >> Nobody. >> So, yeah. It's great. >> Awesome. Keith, thank you so much for joining Dave and me on the program, talking about all of the momentum with HPE Greenlake. >> Always. >> You can't walk in here without feeling the momentum. We appreciate your insights and your time. >> Always. Thank you you for the time. Yeah. Great to see you as well. >> Likewise. >> Thanks. >> For Keith White and Dave Vellante, I'm Lisa Martin. You're watching theCube live, day one coverage from the show floor at HPE Discover '22. We'll be right back with our next guest. (gentle music)

(electronica music) >> Hello everybody. This is Dave Volante and we are covering HPE's big GreenLake announcements. We've got wall-to-wall coverage, a ton of content. We've been watching GreenLake since the beginning. And of one of the things we said early on was let's watch and see how frequently, what the cadence of innovations that HPE brings to the market. Because that's what a cloud company does. So, we're here to welcome you. Keith White is here as the Senior Vice President General Manager of GreenLake cloud services. He runs the commercial business. Keith, thanks for coming on. Help me kick off. >> Thanks for having me. It's awesome to be here. >> So you guys got some momentum orders, 40% growth a year to year on year. You got a lot of momentum, customer growth. >> Yeah, it's fantastic. It's 46%. >> Kyle, thank you for that clarification. And in 46. Big different from 40 to 46. >> No, I think what we're seeing is we're seeing the momentum happen in the marketplace, right? We have a scenario where we're bringing the cloud experience to the customer on their premises. They get to have it automated. Self-serve, easy to consume. They pay for what they use. They can have it in their data center. They can have it at the edge. They can have it at the colo, and, we can manage it all for them. And so they're really getting that true cloud experience and we're seeing it manifest itself in a variety of different customer scenarios. You know, we talked about at Discover, a lot of work that we're doing on the hybrid cloud side of the house, and a lot of work that we're doing on the edge side of things with our partners. But you know, it's exciting to see the explosion of data and how now we're providing this data capability for our customers. >> What are the big trends you're hearing from customers? And how is that informing what you're doing with Green? I mean, I feel like in a lot of ways, Keith, what happened last year, you guys were, were in a better position maybe than most. But what are you hearing and how is that informing your go forward? >> Yeah, I think it's really three things with customers, right? First off, Hey, we're trying to accelerate our digital transformation and it's all becoming about the data. So help us monetize the data, help us protect that data. Help us analyze it to make decisions. And so, you know, number one, it's all about data. Number two is wow, this pandemic, you know, we need to look for cost savings. So, we still need to move our business forward. We've got to accelerate our business, but help me find some cost savings with respect to what I can do. And third, what we're hearing is, hey, we're in a situation, where there's a lot of different capabilities happening with our workforce. They're working from home. They're working hybrid. Help us make sure that we can stay connected to those folks, but also in a secure way, making sure that they have all the tools and resources they need. So those are sort of three of the big themes that we're seeing that GreenLake really helps manifest itself, with the data we're doing now. With all the hybrid cloud capabilities. With the cost savings that we get with respect to our platform, as well as with solutions such as VDI or workforce enablements that we've, we create from a solution standpoint. . >> So, what's the customer reaction, I mean, I mean, everybody now, who's has a big on-premise state, has an as a service capability. A customer saying, oh yeah, oh yeah, how do you make it not me too? In the customer conversations? >> Yeah. I think it turns into, you know, you have to bring the holistic solution to the customer. So yes, there's technology there and we're hearing from, you know, some of the competitors out there. Yeah, we're doing as a service as well, but maybe it's a little bit of storage here. Maybe it's a little bit of networking there. Customers need that end to end solution. And so as you've seen us announce over time, we've got the building blocks, of course, compute storage and networking, but everything runs in a virtual machine. Everything runs in a container or everything runs on the bare metal itself. And that package that we've created for customers means that they can do whatever solution, or whatever workload they want So, if you're a hospital and you're running Epic for your electronic medical records, you can go that route. If you're upgrading SAP and you're using virtual machines at a very large scale, you can use this, use a GreenLake for that as well. So, as you go down the list, there's just so many opportunities with respect to bring those solutions to our customers. And then you bring in our point-next capabilities to support that. You bring in our advisory and professional services, along with our ecosystem to help enable that. You bring in our HPE financial services to help fund that digital transformation. And you've got the complete package. And that's why customers are saying, hey, you guys are now partners of us. You're not just a hardware provider, you're a partner you're helping us solve our business problems and helping us accelerate our business. >> So what should people expect today? You guys got some announcements. What should people look for? >> Well, I think this is, as we've talked about, you know, now we're sort of providing much more capabilities around the data side of the house. Because data is so such, it's the gold, if you will, of a customer's environment. So first off we want to do analytics. So we want an open platform that provides really a unified set of analytics capabilities. And this is where we have a real strong, sweet spot with respect to some of the, the software that we've built around Esperal. But also with the hardware capabilities. As you know, we have all the way up to the Cray supercomputers that, that are doing all of the analytics for whether this or, or financial data that. So, I think that's one of the key things. The second is you got to protect that data. And, and so if it's going to be on prem, I want to know that it's protected and secured. So how do I back it up? How do I have a disaster recovery plan? How do I watch out for ransomware attacks, as well? So we're providing some capabilities there. And then I'd say, lastly, because of all the experience we have with our customers now implementing these hybrid solutions, they're saying, hey, help me with this edge to cloud framework and how do I go and implement that on my own? And so we've taken all the experience and we've bucketed that into our edge to cloud adoption framework to provide that capability for our customers. So we, you know, we're really excited about, again, talking about solutions, talking about accelerating your business, not just talking about technology. >> I said up the top, Keith, that one of the ways I was evaluating you as the pace and the cadence of the innovations. And, and is that, is that fair? How do you guys think about that internally? Are you, you know, you're pushing yourself to go faster, I'm sure you are, but what's that conversation like? >> I think it's a great question because in essence, we're now pivoting the company holistically to being a cloud services and a software company. And that's really exciting and we're seeing that happen internally. But this pace of innovation is really built on what customers are asking us for us. So now that we've grown over 1200 customers worldwide. You know, over $5 billion of total contract value. You know, signing some, some large deals in a variety of solutions and workloads and verticals, et cetera. What we're now seeing is, hey, this is what we need. Help me with my internal IT out to my business groups. Help me with my edge strategy as I build the factory of the future, or, you know, help me with my data and analytics that I'm trying to accomplish for my, you know, diagnosis of, of x-rays and, and capabilities such as Carestream, if you will. So it's, it's exciting to see them come to us and say, this is the capabilities that we're requiring, and we've got our foot on the gas to provide that innovation. And we're miles ahead of the competition. >> All right, we've got an exciting day ahead. We got all kinds of technology discussions, solution discussions. We got, we got, we're going to hear from the analyst community. Really bringing you the, the full package of announcements here. Keith, thanks for helping me set this up. >> Always. Yeah. Thanks so much for having me. >> I look forward today. And thank you for watching. Keep it right there. Tons of content coming your way. You're watching The Cubes coverage of HP's big GreenLake announcement. Right back. (electronica music)

>>approximately two years after HP split into two separate companies, antonioni Ranieri was named president and Ceo of Hewlett Packard Enterprise. Under his tenure, the company has streamlined its operations, sharpened his priorities, simplified the product portfolio and strategically aligned its human capital with key growth initiatives. He's made a number of smaller but high leverage acquisitions and return the company to growth while affecting a massive company wide pivot to an as a service model. Welcome back to HPD discovered 2021. This is Dave Volonte for the cube and it's my pleasure to welcome back Antonio. Neary to the program Antonio it's been a while. Great to see you again. >>Dave Thanks for having me. >>That's really our pleasure. I was just gonna start off with >>the big picture. >>Let's talk about trends. You're a trend spotter. What do you see today? Everybody talks about digital transformation. We had to force marks to digital last year now it's really come into focus. But what are the big trends that you're seeing that are affecting your customers transformations? >>Okay. I mean obviously we have been talking about digital transformation for some time uh in our view is no longer a priority is a strategic imperative. And through the last 15 months or so since we have been going through the pandemic we have seen that accelerated to a level we haven't never seen before. And so what's going on is that we live in a digital economy and through the pandemic now we are more connected than ever. We are much more distributed than ever before and an enormous amount of data is being created and that data has tremendous value. And so what we see in our customers need more connectivity, they need a platform from the edge to the cloud to manage all the data and most important they need to move faster and extracting that inside that value from the data and this is where HP is uniquely positioned to deliver against those experiences the way we haven't imagined before. >>Yeah, we're gonna dig into that now, of course, you and I have been talking about data and how much data for decades, but I feel like we're gonna look back at, you know, in 2030 and say, Wow, we never, we're not gonna do anything like that. So we're really living in a data centric era as the curves are going exponential. What do you see? How do you see customers handling this? How are they thinking about the opportunities? >>Well, I think, you know, customer realized now that they need to move faster, they need to absolutely be uh much more agile and everything. They do. They need to deploy a cloud experience for all the war clothes and data that they manage and they need to deliver business outcomes to stay ahead of the competition. And so we believe technology now plays even a bigger role and every industry is a technology industry in many ways. Every company right, is a technology company, whether your health care, your manufacturer, your transportation company, you are an education, everybody needs more. It no less I. T. But at the same time they want the way they want to consumer Dave is very different than ever before, right? They want an elastic consumption model and they want to be able to scale up and down based on the needs of their enterprise. But if you recall three years ago I knew and I had this conversation, I predicted that enterprise of the future will be edge centric cloud enable and data driven. The edge is the next frontier. We said in 2018 and think about it, you know, people now are working remotely and that age now is much more distribute than we imagined before. Cloud is no longer a destination, it is an experience for all your apps and data, but now we are entering what we call the edge of insight which is all about that data driven approach and this is where all three have to come together in ways that customer did envision before and that's why they need help. >>So I see that I see the definition of cloud changing, it's no longer a set of remote services, you know, somewhere up there in the cloud, it's expanding on prem cross clouds, you mentioned the Edge and so that brings complexity. Every every company is a technology company but they may not be great at technology. So it seems that there are some challenges around there, partly my senses, some of some of what you're trying to do is simplify that for your customers. But what are the challenges that your customers are asking you to solve? >>Well the first they want a consistent and seamless experience, whatever that application and data lives. And so um you know for them you know they want to move away from running I. T. to innovate in our 90 and then obviously they need to move much faster. As I said earlier about this data driven approaches. So they need help because obviously they need to digitize every every aspect of the company but at the same time they need to do it in a much more cost effective way. So they're asking for subject matter expertise on process engineering. They're asking for the fighting the right mix of hybrid experiences from the edge to cloud and they need to move much faster as scale in deploying technologies like Ai deep learning and machine learning. Hewlett Packard Enterprise uh is extremely well positioned because we have been building an age to cloud platform where you provide connectivity where you bring computing and storage uh in a soft of the fine scalable way that you can consume as a service. And so we have great capabilities without HP Point next technology services and advice and run inside. But we have a portfolio with HP Green Lake, our cloud services, the cloud that comes to you that are addressing the most critical data driven warlords. >>Probably about 24 months ago you announced that HP was, was going to basically go all in on as a service and get there by by 2022 for all your solutions. I gotta get, I gotta say you've done a good job communicating the Wall Street, I think. I think culturally you've really done a good job of emphasizing that to your, to the workforce. Uh, but but how should we measure the progress that you've made toward that goal? How our customers responding? I know how the markets responding, you know, three or four year big competitors have now announced. But how should we measure, you know, how you're tracking to that goal? >>Well, I think, you know, the fact that our competitors are entering the other service market is a validation that our vision was right. And that's that's that's good because in the end, you know, it tells us we are on the right track. However, we have to move much faster than than ever before. And that's why we constantly looking for ways to go further and faster. You're right. The court of this is a cultural transformation. Engineering wise, once you step, once you state the North Star, we need to learn our internal processes to think cloud first and data first versus infrastructure. And we have made great progress. The way we measure ourselves. Dave is very simple is by giving a consistent and transparent report on our pivot in that financial aspect of it, which is what we call the annualized revenue run rate, Which we have been disclosed enough for more than a year and a half. And this past quarter grew 30% year over year. So we are on track to deliver at 30 to 40% cake or that we committed two years ago And this business going to triple more than uh more than one year from now. So it's gonna be three times as bigger as we enter 2022 and 2023. But in the end it's all about the experience you deliver and that's why architecturally uh while we made great progress. I know there is way more work to be done, but I'm really excited because what we just announced here this week is just simply remarkable. And you will see more as we become more a cloud operating driven company in the next month and years to come. >>I want to ask you kind of a personal question. I mean, COVID-19 has sharpened our sensitivity and empathy to a lot of different things. And I think ceos in your position of a large tech company or any large company, they really can't just give lip service to things like E. S. G. Or or ethical uh digital transformation, which is something that you've talked about in other words, making sure that it's inclusive. Everybody is able to participate in this economy and not get left behind. What does this mean to you personally? >>Well, they remember I'm in a privileged position, right? Leading a company like Hewlett Packard Enterprise that has Hewlett and Packard on the brand is an honor, but it's also a big responsibility. Let's remember what this company stands for and what our purpose is, which is to advance the way people live and work. And in that we have to be able to create a more equitable society and use this technology to solve some of the biggest societal challenge you have been facing Last 18 months has been really hard on a number of dimensions, not just for the business but for their communities. Uh, we saw disruption, we saw hardships on the financial side, we saw acts of violence and hatred. Those are completely unacceptable. But if we work together, we can use these technologies to bring the community together and to make it equitable. And that's one is one of my passion because as we move into this digital economy, I keep saying that connecting people is the first step and if you are not connected you're not going to participate. Therefore we cannot afford to create a digital economy for only few. And this is why connectivity has to become an essential service, not different than water and electricity. And that's why I have passion and invest my own personal time working with entities like World Economic Forum, educating our government, which is very important because both the public sector and the private sector have to come together. And then from the technology standpoint, we have to architect these things. They are commercially accessible and viable to everyone. And so it's uh it's I will say that it's not just my mission. Uh this is top of mind for many of my colleagues ceos that talked all the time and you can see of movement, but at the same time it's good for business because shareholders now want to invest in companies that take care about this. How we make, not just a world more inclusive and equitable, but also how we make a more sustainable and we with our technologies we can make the world way more sustainable with circular economy, power, efficiency and so forth. So a lot of work to be done dave but I'm encouraged by the progress but we need to do way way more. >>Thank you for that Antonio I want to ask you about the future and I want to ask you a couple of different angles. So I want to start with the edge. So it seems to me that you're you're building this vision of what I call a layer that abstracts the underlying complexity of the whether it's the public cloud across clouds on prem and and and the edge And it's your job to simplify that. So I as the customer can focus on more strategic initiatives and that's clearly the vision that you guys are setting forth on. My question is is how far do you go on the edge? In other words, it seems to me that Aruba for example, for example, awesome acquisition can go really, really deep into the far edge. Maybe other parts of your portfolio, you're kind of more looking at horizontal. How should we think about HP es positioning and participation in that edge opportunity? >>Well, we believe we are becoming one of the merger leaders at the intelligent edge. Right. These edges becoming more intelligent. We live in a hyper connected world and that will continue to grow at an exponential pace. Right? So today we we might have billions of people and devices pursue. We're entering trillions of things that will be connected to the network. Uh, so you need a platform to be able to do with the scale. So there is a horizontal view of that to create these vertical experiences which are industry driven. Right? So one thing is to deliver a vertical experience in healthcare versus manufacturer transportation. And so we take a really far dave I mean, to the point that we just, you know, put into space 256 miles above the earth, a supercomputer that tells you we take a really far, but in the end it's about acting where the data is created and bringing that knowledge and that inside to the people who can make a difference real time as much as possible. And that's why I start by connecting things by bringing a cloud experience to that data wherever it lives because it's cheaper and it's where more economical and obviously there is aspects of latest in security and compliance that you have to deal with it and then ultimately accelerate that inside into some sort of outcome and we have many, many use cases were driving today and Aruba is the platform by the way, which we have been using now to extend from the edge all the way to the core into the cloud business and that's why you HP has unique set of assets to deliver against that opportunity. >>Yes, I want to talk about some of the weapons you have in your arsenal. You know, some people talk about a week and we have to win the architectural battle for hybrid cloud. I've heard that statement made, certainly HPV is in that balance is not a zero sum game, but but you're a player there. And so when I when I look at as a service, great, you're making progress there. But I feel like there's more, there's there's architecture there, you're making acquisitions, you're building out as moral, which is kind of an interesting data platform. Uh, and so I want to ask you, so how you see the architecture emerging and where H. P. S sort of value add i. P. Is your big player and compute you've got actually you've got chops and memory disaggregate asian, you've done custom silicon over the years. How how should we think about your contribution to the next decade of innovation? >>Well, I think it's gonna come different layers of what we call the stock, right? Obviously, uh, we have been known for an infrastructure company, but the reality is what customers are looking for Our integrated solutions that are optimized for the given workload or application. So they don't have to spend time bringing things together. Right? And and spend weeks sometimes months when they can do it in just in a matter of minutes a day so they can move forward innovative or 90. And so we we are really focused on that connectivity as the first step. And Aruba give us an enormous rich uh through the cloud provisioning of a port or a wifi or a one. As you know, as we move to more cloud native applications. Much of the traffic through the connectivity will go into the internet, not through the traditional fixed networks. And that's what we did acquisitions like Silver Peak because now we can connect all your ages and all your clouds in an autonomous software defined way as you go to the other spectrum, right. We talk about what load optimization and uh for us H. P. S. My role is the recipe by which we bring the infrastructure and the software in through that integrated solution that can run autonomously that eventually can consume as a service. And that's why we made the introduction here of HP Green like lighthouse which is actually I fully optimised stack the with the push of a bottom from HP Green Lake cloud platform we can deploy whatever that that is required and then be able to Federated so we can also address other aspects like disaster recovery and be able to share all the knowledge real time. So I'm learning is another thing that people don't understand. I mean if you think about it. So I'm learning is a distributed Ai learning uh ecosystem and think about what we did with the D. C. Any in order to find cures for Alzheimer's or dementia. But swam learning is gonna be the next platform sitting on this age to cloud architecture so that instead of people worrying about sharing data, what we're doing is actually sharing insights And be able to learn to these millions of data points that they can connect with each other in a secure way. Security is another example, right? So today on an average takes 28 days to find a bridge in your enterprise with project Aurora, which we're gonna make available at the end of the year, by the end of the year. We actually can address zero day attacks within seconds. And then we're work in other areas like disaster recovery when you get attacked. Think about the ransom ramp somewhere that we have seen in the last few weeks, right? You know, God forbid you have to pay for it. But at the same time, recovery takes days and weeks. Sometimes we are working on technology to do it within 23 seconds. So this is where HP can place across all spectrums of the stack. And at the same time, of course, people expect us to innovate in infrastructural layer. That's why we also partnered with companies like Intel, we're with the push of a bottle. If you need more capacity of the court, you don't have to order anything, just push the bottle. We make more calls available so that that will load can perform and when you don't need to shut it off so you don't have to pay for it. And last finalist, you know, I will say for us is all about the consumption availability of our solutions And that's what I said, you know, in 2019 we will make available everything as a service by 2022. You know, we have to say as you know, there is no need to build the church for easter sunday when you can rent it for that day. The point here is to grow elastically and the fact that you don't need to move the data is already a cost savings because cost of aggression data back and forth is enormous and customers also don't want to be locked in. So we have an open approach and we have a through age to cloud architecture and we are focusing on what is most valuable aspect for the customer, which is ultimately the data. >>Thank you for that. One of the other things I wanted to ask you about, and again, another weapon in your arsenal is you mentioned uh supercomputing before up in space where we're on the cusp of exa scale and that's the importance of high performance computing. You know, it used to be viewed as just a niche. I've had some great conversations with Dr go about this, but that really is the big data platform, if you will. Uh can I wonder if you could talk a little bit about how that fits into the future. Your expertise in HPC, you're obviously a leader in that space. What's the fit with this new vision? You're laying out? >>Well, HPC, high performance computer in memory computer are the backbone to be able to manage large data sets at massive scale. Um and, you know, deployed technologies like deep learning or artificial intelligence for this massive amount of data. If we talked about the explosion of data all around us and uh, you know, and the algorithms and the parameters to be able to extract inside from the day is getting way more complex. And so the ability to co locate data and computed a massive scale is becoming a necessity, whether it's in academia, whether it's in the government obviously to protect your, your most valuable assets or whether it is in the traditional enterprise. But that's why with the acquisition of Cray, S. G. I. And our organic business, we are absolutely the undisputed leader to provide the level of capabilities. And that's why we are going to build five of the top six exa scale systems, which is basically be able to process they billion billion, meaning billion square transactions per second. Can you imagine what you can do with that? Right. What type of problems you can go solve climate problems? Right. Um you know, obviously be able to put someone back into the moon and eventually in mars you know, the first step to put that supercomputer as an edge computer into the international space station. It's about being able to process data from the images that take from the ice caps of the, of the earth to understand climate changes. But eventually, if you want to put somebody in in into the Marks planet, you have to be able to communicate with those astronauts as they go and you know, you can't afford the latency. Right? So this is where the type of problems we are really focused on. But HPC is something that we are absolutely uh, super committed. And it's something that honestly we have the full stack from silicon to software to the system performance that nobody else has in the industry. >>Well, I think it's a real tailwind for you because the industry is moving that direction. Everybody talks about the data and workloads are shifting. We used to be uh, I got LTP and I got reporting. Now you look at the workloads, there's so much diversity. So I'll give you the last word. What what really is the most exciting to you about the future of HPV? >>Well, I'm excited about the innovation, will bring it to the market and honestly, as the Ceo, I care about the culture of the company. For me, the last almost 3.5 years have been truly remarkable. As you said at the beginning, we are transforming every aspect of this company. When I became CEO, I had three priorities for myself. One is our customers and partners. That's why we do these events right to communicate, communicate, communicate. Uh they are our North Star, that's why we exist. Uh, second is our innovation right? We compete to win with the best innovation, solving the most complex problems in a sustainable and equitable way. And third is the culture of the company, which are the core is how we do things in our Team members and employees. You know, I represent my colleagues here, the 60,000 strong team members that have incredible passion for our customers and to make a contribution every single day. And so for me, I'm very optimistic about what we see the recovery of the economy and the possibilities of technology. But ultimately, you know, we have to work together hand in hand. Uh and I believe this company now is absolutely on the right track to not just be relevant, but really to make a difference. And remember that in the end we we have to be a force for good. And let's not forget that while we do all of this, we have some farm with technology. We have to also help some uh to address some of the challenges we have seen in the last 18 months. An H. P. E is a whole different company, uh, that you knew 3.5 years ago. >>And as you said, it's, it's knowledge is the right thing to do. It's good. It's good for business Antonio. Neary. Thanks so much for coming back to the cube. Is always a pleasure to see you. >>Thanks for having me Dave >>and thank you for watching this version of HP discover 2021 on the cube. This is David want to keep it right there for more great coverage. >>Mm

>>from around the globe. It's the Cube with digital coverage of HP Green Lake day made possible by Hewlett Packard Enterprise. Okay, we're not gonna dive right into some of the news and get into the Green Lake Announcement details. And with me to do that is Keith White is the senior vice president and general manager for Green Lake Cloud Services and Hewlett Packard Enterprise. Keith, thanks for your time. Great to see you. >>Hey, thanks so much for having me. I'm really excited to be here. >>You're welcome. And so listen, before we get into the hard news, can you give us an update on just Green Lake and the business? How's it going? >>You bet. No, it's fantastic. And thanks, you know, for the opportunity again. And hey, I hope everyone's at home staying safe and healthy. It's been a great year for HP Green Lake. There's a ton of momentum that we're seeing in the market place. Uh, we've booked over $4 billion of total contract value to date, and that's over 1000 customers worldwide, and frankly, it's worldwide. It's in 50 50 different countries, and this is a variety of solutions. Variety of workloads. So really just tons of momentum. But it's not just about accelerating the current momentum. It's really about listening to our customers, staying ahead of their demands, delivering more value to them and really executing on the HB Green Lake. Promise. >>Great. Thanks for that and really great detail. Congratulations on the progress, but I know you're not done. So let's let's get to the news. What do people need to know? >>Awesome. Yeah, you know, there's three things that we want to share with you today. So first is all about it's computing. So I could go into some details on that were actually delivering new industry work clothes, which I think will be exciting for a lot of the major industries that are out there. And then we're expanding RHP capabilities just to make things easier and more effective. So first off, you know, we're excited to announce today, um, acceleration of mainstream as adoption for high performance computing through HP Green Lake. And you know, in essence, what we're really excited about is this whole idea of it's a. It's a unique opportunity to write customers with the power of an agile, elastic paper use cloud experience with H. P s market. See systems. So pretty soon any enterprise will be able to tackle their most demanding compute and did intensive workloads, power, artificial intelligence and machine learning initiatives toe provide better business insights and outcomes and again providing things like faster time to incite and accelerated innovation. So today's news is really, really gonna help speed up deployment of HPC projects by 75% and reduced TCO by upto 40% for customers. >>That's awesome. Excited to learn more about the HPC piece, especially. So tell us what's really different about the news today From your perspective. >>No, that's that's a great thing. And the idea is to really help customers with their business outcomes, from building safer cars to improving their manufacturing lines with sustainable materials. Advancing discovery for drug treatment, especially in this time of co vid or making critical millisecond decisions for those finance markets. So you'll see a lot of benefits and a lot of differentiation for customers in a variety of different scenarios and industries. >>Yeah, so I wonder if you could talk a little bit mawr about specifically, you know exactly what's new. Can you unpack some of that for us? >>You bet. Well, what's key is that any enterprise will be able to run their modeling and simulation work clothes in a fully managed because we manage everything for them pre bundled. So we'll give folks this idea of small, medium and large H p e c h piece services to operate in any data center or in a cold a location. These were close air, almost impossible to move to the public cloud because the data so large or it needs to be close by for Leighton see issues. Oftentimes, people have concerns about I p protection or applications and how they run within that that local environment. So if customers are betting their business on this insight and analytics, which many of them are, they need business, critical performance and experts to help them with implementation and migration as well as they want to see resiliency. >>So is this a do it yourself model? In other words, you know the customers have toe manage it on their own. Or how are you helping there? >>No, it's a great question. So the fantastic thing about HP Green Lake is that we manage it all for the customer. And so, in essence, they don't have to worry about anything on the back end, we can flow that we manage capacity. We manage performance, we manage updates and all of those types of things. So we really make it. Make it super simple. And, you know, we're offering these bundled solutions featuring RHP Apollo systems that are purpose built for running things like modeling and simulation workloads. Um, and again, because it's it's Green Lake. And because it's cloud services, this provides itself. Service provides automation. And, you know, customers can actually, um, manage however they want to. We can do it all for them. They could do some on their own. It's really super easy, and it's really up to them on how they want to manage that system. >>What about analytics? You know, you had a lot of people want to dig deeper into the data. How are you supporting that? >>Yeah, Analytics is key. And so one of the best things about this HPC implementation is that we provide unopened platform so customers have the ability to leverage whatever tools they want to do for analytics. They can manage whatever systems they want. Want to pull data from so they really have a ton of flexibility. But the key is because it's HP Green Lake, and because it's HP es market leading HPC systems, they get the fastest they get the it all managed for them. They only pay for what they use, so they don't need to write a huge check for a large up front. And frankly, they get the best of all those worlds together in order to come up with things that matter to them, which is that true business outcome, True Analytics s so that they could make the decisions they need to run their business. >>Yeah, that's awesome. You guys clearly making some good progress here? Actually, I see it really is a game changer for the types of customers that you described. I mean, particularly those folks that you like. You said You think they can't move stuff into the cloud. They've got to stay on Prem. But they want that cloud experience. I mean, that's that's really exciting. We're gonna have you back in a few minutes to talk about the Green Lake Cloud services and in some of the new industry platforms that you see evolving >>awesome. Thanks so much. I look forward to it. >>Yeah, us too. So Okay, right now we're gonna check out the conversation that I had earlier with Pete Ungaro and Addison Snell on HPC. Let's watch welcome everybody to the spotlight session here green. Late day, We're gonna dig into high performance computing. Let me first bring in Pete Ungaro, Who's the GM for HPC and Mission Critical solutions, that Hewlett Packard Enterprise. And then we're gonna pivot Addison Snell, who is the CEO of research firm Intersect 3. 60. So, Pete, starting with you Welcome. And really a pleasure to have you here. I want to first start off by asking you what is the key trends that you see in the HPC and supercomputing space? And I really appreciate if you could talk about how customer consumption patterns are changing. >>Yeah, I appreciate that, David, and thanks for having me. You know, I think the biggest thing that we're seeing is just the massive growth of data. And as we get larger and larger data sets larger and larger models happen, and we're having more and more new ways to compute on that data. So new algorithms like A. I would be a great example of that. And as people are starting to see this, especially they're going through a digital transformations. You know, more and more people I believe can take advantage of HPC but maybe don't know how and don't know how to get started on DSO. They're looking for how to get going into this environment and many customers that are longtime HBC customers, you know, just consume it on their own data centers. They have that capability, but many don't and so they're looking at. How can I do this? Do I need to build up that capability myself? Do I go to the cloud? What about my data and where that resides. So there's a lot of things that are going into thinking through How do I start to take advantage of this new infrastructure? >>Excellent. I mean, we all know HPC workloads. You're talking about supporting research and discovery for some of the toughest and most complex problems, particularly those that affecting society. So I'm interested in your thoughts on how you see Green Lake helping in these endeavors specifically, >>Yeah, One of the most exciting things about HPC is just the impact that it has, you know, everywhere from, you know, building safer cars and airplanes. Thio looking at climate change, uh, to, you know, finding new vaccines for things like Covic that we're all dealing with right now. So one of the biggest things is how do we take advantage event and use that to, you know, benefit society overall. And as we think about implementing HPC, you know, how do we get started? And then how do we grow and scale as we get more and more capability? So that's the biggest things that we're seeing on that front. >>Yes. Okay, So just about a year ago, you guys launched the Green Lake Initiative and the whole, you know, complete focus on as a service. So I'm curious as to how the new Green Lake services the HPC services specifically as it relates to Greenlee. How do they fit in the H. P s overall high performance computing portfolio and the strategy? >>Yeah, great question. You know, Green Lake is a new consumption model for eso. It's a very exciting We keep our entire HPC portfolio that we have today, but extend it with Green Lake and offer customers you know, expanded consumption choices. So, you know, customers that potentially are dealing with the growth of their data or they're moving toe digital transformation applications they can use green light just easily scale up from workstations toe, you know, manage their system costs or operational costs, or or if they don't have staff to expand their environment. Green Light provides all of that in a manage infrastructure for them. So if they're going from like a pilot environment up into a production environment over time, Green Lake enables them to do that very simply and easily without having toe have all that internal infrastructure people, computer data centers, etcetera. Green Lake provides all that for them so they can have a turnkey solution for HBC. >>So a lot easier entry strategies. A key key word that you use. There was choice, though. So basically you're providing optionality. You're not necessarily forcing them into a particular model. Is that correct? >>Yeah, 100%. Dave. What we want to do is just expand the choices so customers can buy a new choir and use that technology to their advantage is whether they're large or small. Whether they're you know, a startup or Fortune 500 company, whether they have their own data centers or they wanna, you know, use a Coehlo facility whether they have their own staff or not, we want to just provide them the opportunity to take advantage of this leading edge resource. >>Very interesting, Pete. It really appreciate the perspective that you guys have bring into the market. I mean, it seems to me it's gonna really accelerate broader adoption of high performance computing, toe the masses, really giving them an easier entry point I want to bring in now. Addison Snell to the discussion. Addison. He's the CEO is, I said of Intersect 3 60 which, in my view, is the world's leading market research company focused on HPC. Addison, you've been following the space for a while. You're an expert. You've seen a lot of changes over the years. What do you see is the critical aspect in the market, specifically as it relates toward this as a service delivery that we were just discussing with Pete and I wonder if you could sort of work in their the benefits in terms of, in your view, how it's gonna affect HPC usage broadly. Yeah, Good morning, David. Thanks very much for having me, Pete. It's great to see you again. So we've been tracking ah lot of these utility computing models in high performance computing for years, particularly as most of the usage by revenue is actually by commercial endeavors. Using high performance computing for their R and D and engineering projects and the like. And cloud computing has been a major portion of that and has the highest growth rate in the market right now, where we're seeing this double digit growth that accounted for about $1.4 billion of the high performance computing industry last year. But the bigger trend on which makes Green like really interesting is that we saw an additional about a billion dollars worth of spending outside what was directly measured in the cloud portion of the market in in areas that we deemed to be cloud like, which were as a service types of contracts that were still utility computing. But they might be under a software as a service portion of the budget under software or some other managed services type of contract that the user wasn't reported directly is cloud, but it was certainly influenced by utility computing, and I think that's gonna be a really dominant portion of the market going forward. And when we look at growth rate and where the market's been evolving, so that's interesting. I mean, basically, you're saying this, you know, the utility model is not brand new. We've seen that for years. Cloud was obviously a catalyst that gave that a boost. What is new, you're saying is and I'll say it this way. I'd love to get your independent perspective on this is so The definition of cloud is expanding where it's you know, people always say it's not a place, it's an experience and I couldn't agree more. But I wonder if you could give us your independent perspective on that, both on the thoughts of what I just said. But also, how would you rate H. P. E s position in this market? Well, you're right, absolutely, that the definition of cloud is expanding, and that's a challenge when we run our surveys that we try to be pedantic in a sense and define exactly what we're talking about. And that's how we're able to measure both the direct usage of ah, typical public cloud, but also ah more flexible notion off as a service. Now you asked about H P E. In particular, And that's extremely relevant not only with Green Lake but with their broader presence in high performance computing. H P E is the number one provider of systems for high performance computing worldwide, and that's largely based on the breath of H. P s offerings, in addition to their performance in various segments. So picking up a lot of the commercial market with their HP apology and 10 plus, they hit a lot of big memory configurations with Superdome flex and scale up to some of the most powerful supercomputers in the world with the HP Cray X platforms that go into some of the leading national labs. Now, Green Light gives them an opportunity to offer this kind of flexibility to customers rather than committing all it wants to a particular purchase price. But if you want to do position those on a utility computing basis pay for them as a service without committing to ah, particular public cloud. I think that's an interesting role for Green Lake to play in the market. Yeah, it's interesting. I mean earlier this year, we celebrated Exa scale Day with support from HP, and it really is all about a community and an ecosystem is a lot of camaraderie going on in the space that you guys are deep into, Addison says. We could wrap. What should observers expect in this HPC market in this space over the next a few years? Yeah, that's a great question. What to expect because of 2020 has taught us anything. It's the hazards of forecasting where we think the market is going. When we put out a market forecast, we tend not to look at huge things like unexpected pandemics or wars. But it's relevant to the topic here because, as I said, we were already forecasting Cloud and as a service, models growing. Any time you get into uncertainty, where it becomes less easy to plan for where you want to be in two years, three years, five years, that model speaks well to things that are cloud or as a service to do very well, flexibly, and therefore, when we look at the market and plan out where we think it is in 2020 2021 anything that accelerates uncertainty actually is going. Thio increase the need for something like Green Lake or and as a service or cloud type of environment. So we're expecting those sorts of deployments to come in over and above where we were already previously expected them in 2020 2021. Because as a service deals well with uncertainty. And that's just the world we've been in recently. I think there's a great comments and in a really good framework. And we've seen this with the pandemic, the pace at which the technology industry in particular, of course, HP specifically have responded to support that your point about agility and flexibility being crucial. And I'll go back toe something earlier that Pete said around the data, the sooner we can get to the data to analyze things, whether it's compressing the time to a vaccine or pivoting our business is the better off we are. So I wanna thank Pete and Addison for your perspectives today. Really great stuff, guys. Thank you. >>Yeah, Thank you. >>Alright, keep it right there from, or great insights and content you're watching green leg day. Alright, Great discussion on HPC. Now we're gonna get into some of the new industry examples and some of the case studies and new platforms. Keith HP, Green Lake It's moving forward. That's clear. You're picking up momentum with customers, but can you give us some examples of platforms for industry use cases and some specifics around that? >>You know, you bet, and actually you'll hear more details from Arwa Qadoura she leads are green like the market efforts in just a little bit. But specifically, I want to highlight some examples where we provide cloud services to help solve some of the most demanding workloads on the planet. So, first off in financial services, for example, traditional banks are facing increased competition and evolving customer expectations they need to transform so that they can reduce risk, manage cop and provided differentiated customer experience. We'll talk about a platform for Splunk that does just that. Second, in health care institutions, they face the growing list of challenges, some due to the cove in 19 Pandemic and others. Years in the making, like our aging population and rise in chronic disease, is really driving up demands, and it's straining capital budgets. These global trance create a critical need for transformation. Thio improve that patient experience and their business outcomes. Another example is in manufacturing. They're facing many challenges in order to remain competitive, right, they need to be able to identify new revenue streams run more efficiently from an operation standpoint and scale. Their resource is so you'll hear more about how we're optimizing and delivery for manufacturing with S. A P Hana and always gonna highlight a little more detail on today's news how we're delivering supercomputing through HP Green Lake It's scale and finally, how we have a robust ecosystem of partners to help enterprises easily deploy these solutions. For example, I think today you're gonna be talking to Skip Bacon from Splunk. >>Yeah, absolutely. We sure are. And some really great examples there, especially a couple industries that that stood out. I mean, financial services and health care. They're ripe for transformation and maybe disruption if if they don't move fast enough. So Keith will be coming back to you a little later today to wrap things up. So So thank you. Now, now we're gonna take a look at how HP is partnering with Splunk and how Green Lake compliments, data rich workloads. Let's watch. We're not going to dig deeper into a data oriented workload. How HP Green Lake fits into this use case and with me, a Skip Bacon vice president, product management at Splunk Skip. Good to see >>you. Good to see you as well there. >>So let's talk a little bit about Splunk. I mean, you guys are a dominant player and security and analytics and you know, it's funny, Skip, I used to comment that during the big data, the rise of big data Splunk really never positioned themselves is this big data player, and you know all that hype. But But you became kind of the leader in big data without really, even, you know, promoting it. It just happened overnight, and you're really now rapidly moving toward a subscription model. You're making some strategic moves in the M and a front. Give us your perspective on what's happening at the company and why customers are so passionate about your software. >>Sure, a great, great set up, Dave. Thanks. So, yeah, let's start with the data that's underneath big data, right? I think I think it is usual. The industry sort of seasons on a term and never stops toe. Think about what it really means. Sure, one big part of big data is your transaction and stuff, right? The things that catch generated by all of your Oracle's USC Cheops that reflect how the business actually occurred. But a much bigger part is all of your digital artifacts, all of the machine generated data that tells you the whole story about what led up to the things that actually happened right within the systems within the interactions within those systems. That's where Splunk is focused. And I think what the market is the whole is really validating is that that machine generated data those digital artifacts are a tely least is important, if not more so, than the transactional artifacts to this whole digital transformation problem right there. Critical to showing I t. How to get better developing and deploying and operating software, how to get better securing these systems, and then how to take this real time view of what the business looks like as it's executing in the software right now. And hold that up to and inform the business and close that feedback loop, right? So what is it we want to do differently digitally in order to do different better on the transformation side of the house. So I think a lot of splints. General growth is proof of the value crop and the need here for sure, as we're seeing play out specifically in the domains of ICTs he operations Dev, ops, Cyber Security, right? As well as more broadly in that in that cloak closing the business loop Splunk spin on its hair and growing our footprint overall with our customers and across many new customers, we've been on its hair with moving parts of that footprints who and as a service offering and spawn cloud. But a lot of that overall growth is really fueled by just making it simpler. Quicker, faster, cheaper, easier toe operates Plunkett scale because the data is certainly not slowing down right. There's more and more and more of it every day, more late, their potential value locked up in it. So anything that we can do and that our partners conducive to improve the cost economics to prove the agility to improve the responsiveness of these systems is huge. That that customer value crop and that's where we get so excited about what's going on with green life >>Yeah, so that makes sense. I mean, the digital businesses, a data business. And that means putting data at the core. And Splunk is obviously you keep part of that. So, as I said earlier, spunk your leader in this space, what's the deal with your HP relationship? You touched on that? What should we know about your your partnership? And what's that solution with H h p E? What's that customer Sweet spot. >>Yep. Good. All good questions. So we've been working with HP for quite a while on on a number of different fronts. This Green lake peace is the most interesting and sort of the intersection of, you know, purist intersection of both of these threads of these factories, if you will. So we've been working to take our core data platform deployed on an enterprise operator for kubernetes. Stick that a top H P s green like which is really kubernetes is a service platform and go prove performance, scalability, agility, flexibility, cost economics, starting with some of slugs, biggest customers. And we've proven, you know, alot of those things In great measure, I think the opportunity you know, the ability to vertically scale Splunk in containers that taught beefy boxes and really streamline the automation, the orchestration, the operations, all of that yields what, in the words of one of our mutual customers, literally put it as This is a transformational platform for deploying and operating spot for us so hard at work on the engineering side, hard at work on the architectural referencing, sizing, you know, capacity planning sides, and then increasing really rolling up our sleeves and taking the stuff the market together. >>Yeah, I mean, we're seeing the just the idea of cloud. The definition of cloud expanding hybrid brings in on Prem. We talked about the edge and and I really We've seen Splunk rapidly transitioning its pricing model to a subscription, you know, platform, if you will. And of course, that's what Green Lakes all about. What makes Splunk a good fit for Green Lake and vice versa? What does it mean for customers? >>Sure, So a couple different parts, I think, make make this a perfect marriage. Splunk at its core, if you're using it well, you're using it in a very iterative discovery driven kind of follow you the path to value basis that makes it a little hard to plan the infrastructure and decides these things right. We really want customers to be focused on how to get more data in how to get more value out. And if you're doing it well, those things, they're going to go up and up and up over time. You don't wanna be constrained by size and capacity planning, procurement cycles for infrastructure. So the Green Lake model, you know, customers got already deployed systems already deployed, capacity available in and as the service basis, very fast, very agile. If they need a next traunch of capacity to bring in that next data set or run, that next set of analytics right it's available immediately is a service, not hey, we've got to kick off the procurement cycle for a whole bunch more hardware boxes. So that flexibility, that agility or key to the general pattern for using Splunk and again that ability to vertically scale stick multiple Splunk instances into containers and load more and more those up on these physical boxes right gives you great cost economics. You know, Splunk has a voracious appetite for data for doing analytics against that data less expensive, we can make that processing the better and the ability to really fully sweat, you know, sweat the assets fully utilize those assets. That kind of vertical scale is the other great element of the Green Lake solution. >>Yes. I mean, when you think about the value prop for for customers with Splunk and HP green, that gets a lot of what you would expect from what we used to talk about with the early days of cloud. Uh, that that flexibility, uh, it takes it away. A lot of the sort of mundane capacity planning you can shift. Resource is you talked about, you know, scale in a in a number of of use cases. So that's sort of another interesting angle, isn't it? >>Yeah. Faster. It's the classic text story. Faster, quicker, cheaper, easier, right? Just take in the whole whole new holy levels and hold the extremes with these technologies. >>What do you see? Is the differentiators with Splunk in HP, Maybe what's different from sort of the way we used to do things, but also sort of, you know, modern day competition. >>Yeah. Good. All good. All good questions. So I think the general attributes of splinter differentiated green Laker differentiated. I think when you put them together, you get this classic one plus one equals three story. So what? I hear from a lot of our target customers, big enterprises, big public sector customers. They can see the path to these benefits. They understand in theory how these different technologies would work together. But they're concerned about their own skills and abilities to go building. Run those and the rial beauty of Green Lake and Splunk is this. All comes sort of pre design, pre integrated right pre built HP is then they're providing these running containers as a service. So it's taking a lot of the skills and the concerns off the customers plate right, allowing them to fast board to, you know, cutting edge technology without any of the wrist. And then, most importantly, allowing customers to focus their very finite resource is their peoples their time, their money, their cycles on the things that are going to drive differentiated value back to the business. You know, let's face facts. Buying and provisioning Hardware is not a differentiating activity, running containers successfully, not differentiating running the core of Splunk. Not that differentiating. He can take all of those cycles and focus them instead on in the simple mechanics. How do we get more data in? Run more analytics on it and get more value out? Right then you're on the path to really delivering differentiated, you know, sustainable competitive basis type stuff back to the business, back to that digital transformation effort. So taking the skills out, taking the worries out, taking the concerns about new tech, out taking the procurement cycles, that improving scalability again quicker, faster, cheaper. Better for sure. >>It's kind of interesting when you when you look at the how the parlance has evolved from cloud and then you had Private Cloud. We talk a lot about hybrid, but I'm interested in your thoughts on why Splunk and HP Green Light green like now I mean, what's happening in the market that makes this the right place and in the right time, so to speak. >>Yeah, again, I put cloud right up there with big data is one of those really overloaded terms. Everything we keep keep redefining as we go if we define it. One way is as an experience instead of outcomes that customers looking for right, what does anyone of our mutual customers really want Well, they want capabilities that air quick to get up and running that air fast, to get the value that are aligned with how the price wise, with how they deliver value to the business and that they can quickly change right as the needs of the business and the operation shift. I think that's the outcome set that people are looking thio. Certainly the early days of cloud we thought were synonymous with public cloud. And hey, the way that you get those outcomes is you push things out. The public cloud providers, you know, what we saw is a lot of that motion in cases where there wasn't the best of alignment, right? You didn't get all those outcomes that you were hoping for. The cost savings weren't there or again. These big enterprises, these big organizations have a whole bunch of other work clothes that aren't necessarily public cloud amenable. But what they want is that same cloud experience. And this is where you see the evolution in the hybrid clouds and into private clouds. Yeah, any one of our customers is looking across the entirety of this landscape, things that are on Prem that they're probably gonna be on Prem forever. Things that they're moving into private cloud environments, things that they're moving into our growing or expanding or landing net new public cloud. They want those same outcomes, the same characteristics across all of that. That's a lot of Splunk value. Crop is a provider, right? Is we can go monitor and help you operate and developed and secure exactly all of that, no matter where it's located. Splunk on Green Lake is all about that stack, you know, working in that very cloud native way even where it made sense for customers to deploy and operate their own software. Even if this want, they're running over here themselves is hoping the modern, secure other work clothes that they put into their public cloud environments. >>Well, it Z another key proof point that we're seeing throughout the day here. Your software leader, you know, HP bring it together. It's ecosystem partners toe actually deliver tangible value. The customers skip. Great to hear your perspective today. Really appreciate you coming on the program. >>My pleasure. And thanks so much for having us take care. Stay well, >>Yeah, Cheers. You too. Okay, keep it right there. We're gonna go back to Keith now. Have him on a close out this segment of the program. You're watching HP Green Lake Day on the Cube. All right, We're So we're seeing some great examples of how Green Lake is supporting a lot of different industries. A lot of different workloads we just heard from Splunk really is part of the ecosystem. Really? A data heavy workload. And we're seeing the progress. HPC example Manufacturing. We talked about healthcare financial services, critical industries that are really driving towards the subscription model. So, Keith, thanks again for joining us. Is there anything else that we haven't hit that you feel are audience should should know about? >>Yeah, you bet. You know, we didn't cover some of the new capabilities that are really providing customers with the holistic experience to address their most demanding workloads with HP Green Lake. So first is our Green Lake managed security services. So this provides customers with an enterprise grade manage security solution that delivers lower costs and frees up a lot of their resource is the second is RHP advisory and Professional Services Group. So they help provide customers with tools and resource is to explore their needs for their digital transformation. Think about workshops and trials and proof of concepts and all of that implementation. Eso You get the strategy piece, you get the advisory piece, and then you get the implementation piece that's required to help them get started really quickly. And then third would be our H. P s moral software portfolio. So this provides customers with the ability to modernize their absent data unify, hybrid cloud and edge computing and operationalized artificial intelligence and machine learning and analytics. >>You know, I'm glad that you brought in the sort of machine intelligence piece in the machine learning because that's, ah, lot of times. That's the reason why people want to go to the cloud at the same time you bring in the security piece a lot of reasons why people want to keep things on Prem. And, of course, the use cases here. We're talking about it, really bringing that cloud experience that consumption model on Prem. I think it's critical critical for companies because they're expanding their notion of cloud computing really extending into hybrid and and the edge with that similar experience or substantially the same experience. So I think folks are gonna look at today's news as real progress. We're pushing you guys on some milestones and some proof points towards this vision is a critical juncture for organizations, especially those look, they're looking for comprehensive offerings to drive their digital transformations. Your thoughts keep >>Yeah, I know you. You know, we know as many as 70% of current and future APS and data are going to remain on Prem. They're gonna be in data centers, they're gonna be in Colo's, they're gonna be at the edge and, you know, really, for critical reasons. And so hybrid is key. As you mentioned, the number of times we wanna help customers transform their businesses and really drive business outcomes in this hybrid, multi cloud world with HP Green Lake and are targeted solutions. >>Excellent. Keith, Thanks again for coming on the program. Really appreciate your time. >>Always. Always. Thanks so much for having me and and take Take care of. Stay healthy, please. >>Alright. Keep it right there. Everybody, you're watching HP Green Lake day on the Cube

>> Announcer: From around the globe it's theCUBE with digital coverage of HPE GreenLake Day made possible by Hewlett Packard Enterprise. >> Welcome everybody to this spotlight session here at GreenLake Day and we're going to dig into high-performance computing. Let me first bring in Pete Ungaro who's the GM for HPC and Mission Critical Solutions at Hewlett Packard Enterprise. And then we're going to pivot to Addison Snell, who's the CEO of research firm Intersect360. So Pete started with you welcome and really a pleasure to have you here. I want to first start off by asking you what are the key trends that you see in the HPC and super computing space. And I really appreciate if you could talk about how customer consumption patterns are changing. >> Yeah, appreciate that Dave and thanks for having me. I think the biggest thing that we're seeing is just the massive growth of data. And as we get larger and larger data sets larger and larger models happen and we're having more and more new ways to compute on that data. So new algorithms like AI would be a great example of that. And as people are starting to see this, especially as they're going through digital transformations, more and more people I believe can take advantage of HPC but maybe don't know how and don't know how to get started. And so they're looking for how to get going into this environment. And many customers that are long-time HPC customers just consume it on their own data centers, they have that capability but many don't. And so they're looking at how can I do this? Do I need to build up that capability myself? Do I go to the Cloud? What about my data and where that resides? So there's a lot of things that are going into thinking through how do I start to take advantage of this new infrastructure? >> Excellent, I mean, we all know HPC workloads. You're talking about fording research and discovery for some of the toughest and most complex problems particularly those that are affecting society. So I'm interested in your thoughts on how you see GreenLake helping in these endeavors specifically. >> Yeah, one of the most exciting things about HPC is just the impact that it has. Everywhere from building safer cars and airplanes to looking at climate change to finding new vaccines for things like COVID that we're all dealing with right now. So one of the biggest things is how do we take advantage of that and use that to benefit society overall. And as we think about implementing HPC, how do we get started and then how do we grow and scale as we get more and more capabilities. So that's the biggest things that we're seeing on that front. >> Yeah, okay, so just about a year ago you guys launched the GreenLake initiative and the whole complete focus on as a service. So I'm curious as to how the new GreenLake services the HPC services specifically as it relates to GreenLake, how do they fit into HP's overall high-performance computing portfolio and the strategy? >> Yeah, great question. GreenLake is a new consumption model for us. So it's a very exciting. We keep our entire HPC portfolio that we have today but extend it with GreenLake and offer customers expanded consumption choices. So customers that potentially are dealing with the growth of their data or they're moving to digital transformation applications, they can use GreenLake just easily scale up from workstations to manage their system costs or operational costs or if they don't have staff to expand their environment, GreenLake provides all of that in a managed infrastructure for them. So if they're going from like a pilot environment, I've been to a production environment over time, GreenLake enables them to do that very simply and easily without having to have all that internal infrastructure people, computer data centers, et cetera, GreenLake provides all that for them. So they can have a turnkey solution for HPC. >> So a lot easier entry strategy is a key word that you use there was choice though. So basically you're providing optionality, you're not necessarily forcing them into a particular model, is that correct? >> Yeah, 100% Dave. What we want to do is just expand the choices so customers can buy and acquire and use that technology to their advantages. Whether they're large or small, whether they're a startup or a fortune 500 company, whether they have their own data centers or they want to use a colo facility, whether they have their own staff or not. We want to just provide them the opportunity to take advantage of this leading edge resource. >> Very interesting, Pete, I really appreciate the perspectives that you guys are bringing to the market. I mean, it seems to me it's going to really accelerate broader adoption of high-performance computing to the masses, really giving them an easier entry point. I want to bring in now Addison Snell to the discussion. Addison, he's a CEO, as I said of Intersect360 which in my view is the world's leading market research company focused on HPC. Addison you've been following this space for a while. You're an expert, you've seen a lot of changes over the years. What do you see as the critical aspects in the market specifically as it relates toward this as a service delivery that we were just discussing with Pete? And I wonder if you could sort of work in there the benefits in terms of in your view how it's going to affect HPC usage broadly. >> Yeah, good morning Dave, and thanks very much for having me. Pete it's great to see you again. So we've been tracking a lot of these utility computing models in high-performance computing for years. Particularly as most of the usage by revenue is actually by commercial endeavors using high-performance computing for their R and D and engineering projects and the like. And cloud computing has been a major portion of that and has the highest growth rate in the market right now where we're seeing this double digit growth that accounted for about $1.4 billion of the high-performance computing industry last year. But the bigger trend and which makes GreenLake really interesting is that we saw an additional about a billion dollars worth of spending outside what was directly measured in the cloud portion of the market in areas that we deemed to be cloud-like which were as a service types of contracts that were still utility computing, but they might be under a software as a service portion of a budget under software or some other managed services type of contract that the user wasn't reporting directly as cloud but was certainly influenced by utility computing. And I think that's going to be a really dominant portion of the market going forward when we look at a growth rate and where the market's been evolving. >> So that's interesting. I mean, basically you're saying this utility model is not brand new, we've seen that for years. Cloud was obviously a catalyst that gave that a boost. What is new you're saying is, and I'll say it this way. I'd love to get your independent perspective on this is sort of the definition of cloud is expanding where we people always say, it's not a place, it's an experience and I couldn't agree more. But I wonder if you could give us your independent perspective on that, both on the thoughts of what I just said but also how would you rate HPE position in this market? >> Well, you're right absolutely that the definition of cloud is expanding. And that's a challenge when we run our surveys that we try to be pedantic in a sense and define exactly what we're talking about. And that's how we're able to measure both the direct usage of a typical public cloud but also a more flexible notion of as a service. Now you asked about HPE in particular and that's extremely relevant, not only with GreenLake, but with their broader presence in high-performance computing. HPE is the number one provider of systems for high-performance computing worldwide. And that's largely based on the breadth of HPE's offerings in addition to their performance at various segments. So picking up a lot of the commercial market with our HPE Apollo Gen10 plus, they hit a lot of big memory configurations with the Superdome Flex and scale up to some of the most powerful supercomputers in the world with the HPE Cray EX platforms that go into some of the leading national labs. Now GreenLake gives them an opportunity to offer this kind of flexibility to customers rather than committing all at once to a particular purchase price. But if you want to do position those on a utility computing basis, pay for them as a service without committing to a particular public cloud, I think that's an interesting role for GreenLake to play in the market. >> Yeah, yeah it's interesting. I mean, earlier this year we celebrated Exascale Day with the support from HPE and it really is all about a community and an ecosystem. Is a lot of comradery going on in the space that you guys are deep into. Addison, it says we can wrap what should observe as expect in this HPC market, in this space over the next few years? >> Yeah, that's a great question what to expect because if 2020 has taught us anything it's the hazards of forecasting where we think the market is going. Like when we put out a market forecast, we tend not to look at huge things like unexpected pandemics or wars but it's relevant to the topic here. Because as I said, we were already forecasting cloud and as a service models growing. Anytime you get into uncertainty where it becomes less easy to plan for where you want to be in two years, three years, five years, that model speaks well to things that are cloud or as a service to do very well flexibly. And therefore, when we look at the market and plan out where we think it is in 2020, 2021, anything that accelerates uncertainty actually is going to increase the need for something like GreenLake or an as a service or cloud type of environment. So we're expecting those sorts of deployments to come in over and above where we were already previously expected them in 2020, 2021. Because as a service deals well with uncertainty and that's just the world we've been in recently. >> I think those are great comments and a really good framework. And we've seen this with the pandemic, the pace at which the technology industry in particular and of course HPE specifically have responded to support that. Your point about agility and flexibility being crucial. And I'll go back to something earlier that Pete said around the data, the sooner we can get to the data to analyze things, whether it's compressing the time to a vaccine or pivoting our businesses, the better off we are. So I want to thank Pete and Addison for your perspectives today. Really great stuff, guys, thank you. >> Yeah, thank you. >> Thank you. >> All right, keep it right there for more great insights and content. You're watching GreenLake Day. (ambient music)

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

from around the globe it's thecube with digital coverage of exascale day made possible by hewlett packard enterprise welcome everyone to the cube celebration of exascale day ben bennett is here he's an hpc strategist and evangelist at hewlett-packard enterprise ben welcome good to see you good to see you too son hey well let's evangelize exascale a little bit you know what's exciting you uh in regards to the coming of exoskilled computing um well there's a couple of things really uh for me historically i've worked in super computing for many years and i have seen the coming of several milestones from you know actually i'm old enough to remember gigaflops uh coming through and teraflops and petaflops exascale is has been harder than many of us anticipated many years ago the sheer amount of technology that has been required to deliver machines of this performance has been has been us utterly staggering but the exascale era brings with it real solutions it gives us opportunities to do things that we've not been able to do before if you look at some of the the most powerful computers around today they've they've really helped with um the pandemic kovid but we're still you know orders of magnitude away from being able to design drugs in situ test them in memory and release them to the public you know we still have lots and lots of lab work to do and exascale machines are going to help with that we are going to be able to to do more um which ultimately will will aid humanity and they used to be called the grand challenges and i still think of them as that i still think of these challenges for scientists that exascale class machines will be able to help but also i'm a realist is that in 10 20 30 years time you know i should be able to look back at this hopefully touch wood look back at it and look at much faster machines and say do you remember the days when we thought exascale was faster yeah well you mentioned the pandemic and you know the present united states was tweeting this morning that he was upset that you know the the fda in the u.s is not allowing the the vaccine to proceed as fast as you'd like it in fact it the fda is loosening some of its uh restrictions and i wonder if you know high performance computing in part is helping with the simulations and maybe predicting because a lot of this is about probabilities um and concerns is is is that work that is going on today or are you saying that that exascale actually you know would be what we need to accelerate that what's the role of hpc that you see today in regards to sort of solving for that vaccine and any other sort of pandemic related drugs so so first a disclaimer i am not a geneticist i am not a biochemist um my son is he tries to explain it to me and it tends to go in one ear and out the other um um i just merely build the machines he uses so we're sort of even on that front um if you read if you had read the press there was a lot of people offering up systems and computational resources for scientists a lot of the work that has been done understanding the mechanisms of covid19 um have been you know uncovered by the use of very very powerful computers would exascale have helped well clearly the faster the computers the more simulations we can do i think if you look back historically no vaccine has come to fruition as fast ever under modern rules okay admittedly the first vaccine was you know edward jenner sat quietly um you know smearing a few people and hoping it worked um i think we're slightly beyond that the fda has rules and regulations for a reason and we you don't have to go back far in our history to understand the nature of uh drugs that work for 99 of the population you know and i think exascale widely available exoscale and much faster computers are going to assist with that imagine having a genetic map of very large numbers of people on the earth and being able to test your drug against that breadth of person and you know that 99 of the time it works fine under fda rules you could never sell it you could never do that but if you're confident in your testing if you can demonstrate that you can keep the one percent away for whom that drug doesn't work bingo you now have a drug for the majority of the people and so many drugs that have so many benefits are not released and drugs are expensive because they fail at the last few moments you know the more testing you can do the more testing in memory the better it's going to be for everybody uh personally are we at a point where we still need human trials yes do we still need due diligence yes um we're not there yet exascale is you know it's coming it's not there yet yeah well to your point the faster the computer the more simulations and the higher the the chance that we're actually going to going to going to get it right and maybe compress that time to market but talk about some of the problems that you're working on uh and and the challenges for you know for example with the uk government and maybe maybe others that you can you can share with us help us understand kind of what you're hoping to accomplish so um within the united kingdom there was a report published um for the um for the uk research institute i think it's the uk research institute it might be epsrc however it's the body of people responsible for funding um science and there was a case a science case done for exascale i'm not a scientist um a lot of the work that was in this documentation said that a number of things that can be done today aren't good enough that we need to look further out we need to look at machines that will do much more there's been a program funded called asimov and this is a sort of a commercial problem that the uk government is working with rolls royce and they're trying to research how you build a full engine model and by full engine model i mean one that takes into account both the flow of gases through it and how those flow of gases and temperatures change the physical dynamics of the engine and of course as you change the physical dynamics of the engine you change the flow so you need a closely coupled model as air travel becomes more and more under the microscope we need to make sure that the air travel we do is as efficient as possible and currently there aren't supercomputers that have the performance one of the things i'm going to be doing as part of this sequence of conversations is i'm going to be having an in detailed uh sorry an in-depth but it will be very detailed an in-depth conversation with professor mark parsons from the edinburgh parallel computing center he's the director there and the dean of research at edinburgh university and i'm going to be talking to him about the azimoth program and and mark's experience as the person responsible for looking at exascale within the uk to try and determine what are the sort of science problems that we can solve as we move into the exoscale era and what that means for humanity what are the benefits for humans yeah and that's what i wanted to ask you about the the rolls-royce example that you gave it wasn't i if i understood it wasn't so much safety as it was you said efficiency and so that's that's what fuel consumption um it's it's partly fuel consumption it is of course safety there is a um there is a very specific test called an extreme event or the fan blade off what happens is they build an engine and they put it in a cowling and then they run the engine at full speed and then they literally explode uh they fire off a little explosive and they fire a fan belt uh a fan blade off to make sure that it doesn't go through the cowling and the reason they do that is there has been in the past uh a uh a failure of a fan blade and it came through the cowling and came into the aircraft depressurized the aircraft i think somebody was killed as a result of that and the aircraft went down i don't think it was a total loss one death being one too many but as a result you now have to build a jet engine instrument it balance the blades put an explosive in it and then blow the fan blade off now you only really want to do that once it's like car crash testing you want to build a model of the car you want to demonstrate with the dummy that it is safe you don't want to have to build lots of cars and keep going back to the drawing board so you do it in computers memory right we're okay with cars we have computational power to resolve to the level to determine whether or not the accident would hurt a human being still a long way to go to make them more efficient uh new materials how you can get away with lighter structures but we haven't got there with aircraft yet i mean we can build a simulation and we can do that and we can be pretty sure we're right um we still need to build an engine which costs in excess of 10 million dollars and blow the fan blade off it so okay so you're talking about some pretty complex simulations obviously what are some of the the barriers and and the breakthroughs that are kind of required you know to to do some of these things that you're talking about that exascale is going to enable i mean presumably there are obviously technical barriers but maybe you can shed some light on that well some of them are very prosaic so for example power exoscale machines consume a lot of power um so you have to be able to design systems that consume less power and that goes into making sure they're cooled efficiently if you use water can you reuse the water i mean the if you take a laptop and sit it on your lap and you type away for four hours you'll notice it gets quite warm um an exascale computer is going to generate a lot more heat several megawatts actually um and it sounds prosaic but it's actually very important to people you've got to make sure that the systems can be cooled and that we can power them yeah so there's that another issue is the software the software models how do you take a software model and distribute the data over many tens of thousands of nodes how do you do that efficiently if you look at you know gigaflop machines they had hundreds of nodes and each node had effectively a processor a core a thread of application we're looking at many many tens of thousands of nodes cores parallel threads running how do you make that efficient so is the software ready i think the majority of people will tell you that it's the software that's the problem not the hardware of course my friends in hardware would tell you ah software is easy it's the hardware that's the problem i think for the universities and the users the challenge is going to be the software i think um it's going to have to evolve you you're just you want to look at your machine and you just want to be able to dump work onto it easily we're not there yet not by a long stretch of the imagination yeah consequently you know we one of the things that we're doing is that we have a lot of centers of excellence is we will provide well i hate say the word provide we we sell super computers and once the machine has gone in we work very closely with the establishments create centers of excellence to get the best out of the machines to improve the software um and if a machine's expensive you want to get the most out of it that you can you don't just want to run a synthetic benchmark and say look i'm the fastest supercomputer on the planet you know your users who want access to it are the people that really decide how useful it is and the work they get out of it yeah the economics is definitely a factor in fact the fastest supercomputer in the planet but you can't if you can't afford to use it what good is it uh you mentioned power uh and then the flip side of that coin is of course cooling you can reduce the power consumption but but how challenging is it to cool these systems um it's an engineering problem yeah we we have you know uh data centers in iceland where it gets um you know it doesn't get too warm we have a big air cooled data center in in the united kingdom where it never gets above 30 degrees centigrade so if you put in water at 40 degrees centigrade and it comes out at 50 degrees centigrade you can cool it by just pumping it round the air you know just putting it outside the building because the building will you know never gets above 30 so it'll easily drop it back to 40 to enable you to put it back into the machine um right other ways to do it um you know is to take the heat and use it commercially there's a there's a lovely story of they take the hot water out of the supercomputer in the nordics um and then they pump it into a brewery to keep the mash tuns warm you know that's that's the sort of engineering i can get behind yeah indeed that's a great application talk a little bit more about your conversation with professor parsons maybe we could double click into that what are some of the things that you're going to you're going to probe there what are you hoping to learn so i think some of the things that that are going to be interesting to uncover is just the breadth of science that can be uh that could take advantage of exascale you know there are there are many things going on that uh that people hear about you know we people are interested in um you know the nobel prize they might have no idea what it means but the nobel prize for physics was awarded um to do with research into black holes you know fascinating and truly insightful physics um could it benefit from exascale i have no idea uh i i really don't um you know one of the most profound pieces of knowledge in in the last few hundred years has been the theory of relativity you know an austrian patent clerk wrote e equals m c squared on the back of an envelope and and voila i i don't believe any form of exascale computing would have helped him get there any faster right that's maybe flippant but i think the point is is that there are areas in terms of weather prediction climate prediction drug discovery um material knowledge engineering uh problems that are going to be unlocked with the use of exascale class systems we are going to be able to provide more tools more insight [Music] and that's the purpose of computing you know it's not that it's not the data that that comes out and it's the insight we get from it yeah i often say data is plentiful insights are not um ben you're a bit of an industry historian so i've got to ask you you mentioned you mentioned mentioned gigaflop gigaflops before which i think goes back to the early 1970s uh but the history actually the 80s is it the 80s okay well the history of computing goes back even before that you know yes i thought i thought seymour cray was you know kind of father of super computing but perhaps you have another point of view as to the origination of high performance computing [Music] oh yes this is um this is this is one for all my colleagues globally um you know arguably he says getting ready to be attacked from all sides arguably you know um computing uh the parallel work and the research done during the war by alan turing is the father of high performance computing i think one of the problems we have is that so much of that work was classified so much of that work was kept away from commercial people that commercial computing evolved without that knowledge i uh i have done in in in a previous life i have done some work for the british science museum and i have had the great pleasure in walking through the the british science museum archives um to look at how computing has evolved from things like the the pascaline from blaise pascal you know napier's bones the babbage's machines uh to to look all the way through the analog machines you know what conrad zeus was doing on a desktop um i think i think what's important is it doesn't matter where you are is that it is the problem that drives the technology and it's having the problems that requires the you know the human race to look at solutions and be these kicks started by you know the terrible problem that the us has with its nuclear stockpile stewardship now you've invented them how do you keep them safe originally done through the ascii program that's driven a lot of computational advances ultimately it's our quest for knowledge that drives these machines and i think as long as we are interested as long as we want to find things out there will always be advances in computing to meet that need yeah and you know it was a great conversation uh you're a brilliant guest i i love this this this talk and uh and of course as the saying goes success has many fathers so there's probably a few polish mathematicians that would stake a claim in the uh the original enigma project as well i think i think they drove the algorithm i think the problem is is that the work of tommy flowers is the person who took the algorithms and the work that um that was being done and actually had to build the poor machine he's the guy that actually had to sit there and go how do i turn this into a machine that does that and and so you know people always remember touring very few people remember tommy flowers who actually had to turn the great work um into a working machine yeah super computer team sport well ben it's great to have you on thanks so much for your perspectives best of luck with your conversation with professor parsons we'll be looking forward to that and uh and thanks so much for coming on thecube a complete pleasure thank you and thank you everybody for watching this is dave vellante we're celebrating exascale day you're watching the cube [Music]

>> Narrator: From around the globe, it's theCUBE with digital coverage of Exascale day, made possible by Hewlett Packard Enterprise. >> Hey, welcome back Jeff Frick here with the cube come due from our Palo Alto studios, for their ongoing coverage in the celebration of Exascale day 10 to the 18th on October 18th, 10 with 18 zeros, it's all about big powerful giant computing and computing resources and computing power. And we're excited to invite back our next guest she's been on before. She's Dr. Arti Garg, head of advanced AI solutions and technologies for HPE. Arti great to see you again. >> Great to see you. >> Absolutely. So let's jump into before we get into Exascale day I was just looking at your LinkedIn profile. It's such a very interesting career. You've done time at Lawrence Livermore, You've done time in the federal government, You've done time at GE and industry, I just love if you can share a little bit of your perspective going from hardcore academia to, kind of some government positions, then into industry as a data scientist, and now with originally Cray and now HPE looking at it really from more of a vendor side. >> Yeah. So I think in some ways, I think I'm like a lot of people who've had the title of data scientists somewhere in their history where there's no single path, to really working in this industry. I come from a scientific background. I have a PhD in physics, So that's where I started working with large data sets. I think of myself as a data scientist before the term data scientist was a term. And I think it's an advantage, to be able to have seen this explosion of interest in leveraging data to gain insights, whether that be into the structure of the galaxy, which is what I used to look at, or whether that be into maybe new types of materials that could advance our ability to build lightweight cars or safety gear. It's allows you to take a perspective to not only understand what the technical challenges are, but what also the implementation challenges are, and why it can be hard to use data to solve problems. >> Well, I'd just love to get your, again your perspective cause you are into data, you chose that as your profession, and you probably run with a whole lot of people, that are also like-minded in terms of data. As an industry and as a society, we're trying to get people to do a better job of making database decisions and getting away from their gut and actually using data. I wonder if you can talk about the challenges of working with people who don't come from such an intense data background to get them to basically, I don't know if it's understand the value of more of a data kind decision making process or board just it's worth the effort, cause it's not easy to get the data and cleanse the data, and trust the data and get the right context, working with people that don't come from that background. And aren't so entrenched in that point of view, what surprises you? How do you help them? What can you share in terms of helping everybody get to be a more data centric decision maker? >> So I would actually rephrase the question a little bit Jeff, and say that actually I think people have always made data driven decisions. It's just that in the past we maybe had less data available to us or the quality of it was not as good. And so as a result most organizations have developed organize themselves to make decisions, to run their processes based on a much smaller and more refined set of information, than is currently available both given our ability to generate lots of data, through software and sensors, our ability to store that data. And then our ability to run a lot of computing cycles and a lot of advanced math against that data, to learn things that maybe in the past took, hundreds of years of experiments in scientists to understand. And so before I jumped into, how do you overcome that barrier? Just I'll use an example because you mentioned, I used to work in industry I used to work at GE. And one of the things that I often joked about, is the number of times I discovered Bernoulli's principle, in data coming off a GE jet engines you could do that overnight processing these large data but of course historically that took hundreds of years, to really understand these physical principles. And so I think when it comes to how do we bridge the gap between people who are adapt at processing large amounts of data, and running algorithms to pull insights out? I think it's both sides. I think it's those of us who are coming from the technical background, really understanding the way decisions are currently made, the way process and operations currently work at an organization. And understanding why those things are the way they are maybe their security or compliance or accountability concerns, that a new algorithm can't just replace those. And so I think it's on our end, really trying to understand, and make sure that whatever new approaches we're bringing address those concerns. And I think for folks who aren't necessarily coming from a large data set, and analytical background and when I say analytical, I mean in the data science sense, not in the sense of thinking about things in an abstract way to really recognize that these are just tools, that can enhance what they're doing, and they don't necessarily need to be frightening because I think that people who have been say operating electric grids for a long time, or fixing aircraft engines, they have a lot of expertise and a lot of understanding, and that's really important to making any kind of AI driven solution work. >> That's great insight but that but I do think one thing that's changed you come from a world where you had big data sets, so you kind of have a big data set point of view, where I think for a lot of decision makers they didn't have that data before. So we won't go through all the up until the right explosions of data, and obviously we're talking about Exascale day, but I think for a lot of processes now, the amount of data that they can bring to bear, is so dwarfs what they had in the past that before they even consider how to use it they still have to contextualize it, and they have to manage it and they have to organize it and there's data silos. So there's all this kind of nasty processes stuff, that's in the way some would argue has been kind of a real problem with the promise of BI, and does decision support tools. So as you look at at this new stuff and these new datasets, what are some of the people in process challenges beyond the obvious things that we can think about, which are the technical challenges? >> So I think that you've really hit on, something I talk about sometimes it was kind of a data deluge that we experienced these days, and the notion of feeling like you're drowning in information but really lacking any kind of insight. And one of the things that I like to think about, is to actually step back from the data questions the infrastructure questions, sort of all of these technical questions that can seem very challenging to navigate. And first ask ourselves, what problems am I trying to solve? It's really no different than any other type of decision you might make in an organization to say like, what are my biggest pain points? What keeps me up at night? or what would just transform the way my business works? And those are the problems worth solving. And then the next question becomes, if I had more data if I had a better understanding of something about my business or about my customers or about the world in which we all operate, would that really move the needle for me? And if the answer is yes, then that starts to give you a picture of what you might be able to do with AI, and it starts to tell you which of those data management challenges, whether they be cleaning the data, whether it be organizing the data, what it, whether it be building models on the data are worth solving because you're right, those are going to be a time intensive, labor intensive, highly iterative efforts. But if you know why you're doing it, then you will have a better understanding of why it's worth the effort. And also which shortcuts you can take which ones you can't, because often in order to sort of see the end state you might want to do a really quick experiment or prototype. And so you want to know what matters and what doesn't at least to that. Is this going to work at all time. >> So you're not buying the age old adage that you just throw a bunch of data in a data Lake and the answers will just spring up, just come right back out of the wall. I mean, you bring up such a good point, It's all about asking the right questions and thinking about asking questions. So again, when you talk to people, about helping them think about the questions, cause then you've got to shape the data to the question. And then you've got to start to build the algorithm, to kind of answer that question. How should people think when they're actually building algorithm and training algorithms, what are some of the typical kind of pitfalls that a lot of people fall in, haven't really thought about it before and how should people frame this process? Cause it's not simple and it's not easy and you really don't know that you have the answer, until you run multiple iterations and compare it against some other type of reference? >> Well, one of the things that I like to think about just so that you're sort of thinking about, all the challenges you're going to face up front, you don't necessarily need to solve all of these problems at the outset. But I think it's important to identify them, is I like to think about AI solutions as, they get deployed being part of a kind of workflow, and the workflow has multiple stages associated with it. The first stage being generating your data, and then starting to prepare and explore your data and then building models for your data. But sometimes I think where we don't always think about it is the next two phases, which is deploying whatever model or AI solution you've developed. And what will that really take especially in the ecosystem where it's going to live. If is it going to live in a secure and compliant ecosystem? Is it actually going to live in an outdoor ecosystem? We're seeing more applications on the edge, and then finally who's going to use it and how are they going to drive value from it? Because it could be that your AI solution doesn't work cause you don't have the right dashboard, that highlights and visualizes the data for the decision maker who will benefit from it. So I think it's important to sort of think through all of these stages upfront, and think through maybe what some of the biggest challenges you might encounter at the Mar, so that you're prepared when you meet them, and you can kind of refine and iterate along the way and even upfront tweak the question you're asking. >> That's great. So I want to get your take on we're celebrating Exascale day which is something very specific on 1018, share your thoughts on Exascale day specifically, but more generally I think just in terms of being a data scientist and suddenly having, all this massive compute power. At your disposal yoy're been around for a while. So you've seen the development of the cloud, these huge data sets and really the ability to, put so much compute horsepower against the problems as, networking and storage and compute, just asymptotically approach zero, I mean for as a data scientist you got to be pretty excited about kind of new mysteries, new adventures, new places to go, that we just you just couldn't do it 10 years ago five years ago, 15 years ago. >> Yeah I think that it's, it'll--only time will tell exactly all of the things that we'll be able to unlock, from these new sort of massive computing capabilities that we're going to have. But a couple of things that I'm very excited about, are that in addition to sort of this explosion or these very large investments in large supercomputers Exascale super computers, we're also seeing actually investment in these other types of scientific instruments that when I say scientific it's not just academic research, it's driving pharmaceutical drug discovery because we're talking about these, what they call light sources which shoot x-rays at molecules, and allow you to really understand the structure of the molecules. What Exascale allows you to do is, historically it's been that you would go take your molecule to one of these light sources and you shoot your, x-rays edit and you would generate just masses and masses of data, terabytes of data it was each shot. And being able to then understand, what you were looking at was a long process, getting computing time and analyzing the data. We're on the precipice of being able to do that, if not in real time much closer to real time. And I don't really know what happens if instead of coming up with a few molecules, taking them, studying them, and then saying maybe I need to do something different. I can do it while I'm still running my instrument. And I think that it's very exciting, from the perspective of someone who's got a scientific background who likes using large data sets. There's just a lot of possibility of what Exascale computing allows us to do in from the standpoint of I don't have to wait to get results, and I can either stimulate much bigger say galaxies, and really compare that to my data or galaxies or universes, if you're an astrophysicist or I can simulate, much smaller finer details of a hypothetical molecule and use that to predict what might be possible, from a materials or drug perspective, just to name two applications that I think Exascale could really drive. >> That's really great feedback just to shorten that compute loop. We had an interview earlier in some was talking about when the, biggest workload you had to worry about was the end of the month when you're running your financial, And I was like, why wouldn't that be nice to be the biggest job that we have to worry about? But now I think we saw some of this at animation, in the movie business when you know the rendering for whether it's a full animation movie, or just something that's a heavy duty three effects. When you can get those dailies back to the, to the artist as you said while you're still working, or closer to when you're working versus having this, huge kind of compute delay, it just changes the workflow dramatically and the pace of change and the pace of output. Because you're not context switching as much and you can really get back into it. That's a super point. I want to shift gears a little bit, and talk about explainable AI. So this is a concept that a lot of people hopefully are familiar with. So AI you build the algorithm it's in a box, it runs and it kicks out an answer. And one of the things that people talk about, is we should be able to go in and pull that algorithm apart to know, why it came out with the answer that it did. To me this just sounds really really hard because it's smart people like you, that are writing the algorithms the inputs and the and the data that feeds that thing, are super complex. The math behind it is very complex. And we know that the AI trains and can change over time as you you train the algorithm it gets more data, it adjusts itself. So it's explainable AI even possible? Is it possible at some degree? Because I do think it's important. And my next question is going to be about ethics, to know why something came out. And the other piece that becomes so much more important, is as we use that output not only to drive, human based decision that needs some more information, but increasingly moving it over to automation. So now you really want to know why did it do what it did explainable AI? Share your thoughts. >> It's a great question. And it's obviously a question that's on a lot of people's mind these days. I'm actually going to revert back to what I said earlier, when I talked about Bernoulli's principle, and just the ability sometimes when you do throw an algorithm at data, it might come the first thing it will find is probably some known law of physics. And so I think that really thinking about what do we mean by explainable AI, also requires us to think about what do we mean by AI? These days AI is often used anonymously with deep learning which is a particular type of algorithm that is not very analytical at its core. And what I mean by that is, other types of statistical machine learning models, have some underlying theory of what the population of data that you're studying. And whereas deep learning doesn't, it kind of just learns whatever pattern is sitting in front of it. And so there is a sense in which if you look at other types of algorithms, they are inherently explainable because you're choosing your algorithm based on what you think the is the sort of ground truth, about the population you're studying. And so I think we going to get to explainable deep learning. I think it's kind of challenging because you're always going to be in a position, where deep learning is designed to just be as flexible as possible. I'm sort of throw more math at the problem, because there may be are things that your sort of simpler model doesn't account for. However deep learning could be, part of an explainable AI solution. If for example, it helps you identify what are important so called features to look at what are the important aspects of your data. So I don't know it depends on what you mean by AI, but are you ever going to get to the point where, you don't need humans sort of interpreting outputs, and making some sets of judgments about what a set of computer algorithms that are processing data think. I think it will take, I don't want to say I know what's going to happen 50 years from now, but I think it'll take a little while to get to the point where you don't have, to maybe apply some subject matter understanding and some human judgment to what an algorithm is putting out. >> It's really interesting we had Dr. Robert Gates on a years ago at another show, and he talked about the only guns in the U.S. military if I'm getting this right, that are automatic, that will go based on what the computer tells them to do, and start shooting are on the Korean border. But short of that there's always a person involved, before anybody hits a button which begs a question cause we've seen this on the big data, kind of curve, i think Gartner has talked about it, as we move up from kind of descriptive analytics diagnostic analytics, predictive, and then prescriptive and then hopefully autonomous. So I wonder so you're saying will still little ways in that that last little bumps going to be tough to overcome to get to the true autonomy. >> I think so and you know it's going to be very application dependent as well. So it's an interesting example to use the DMZ because that is obviously also a very, mission critical I would say example but in general I think that you'll see autonomy. You already do see autonomy in certain places, where I would say the States are lower. So if I'm going to have some kind of recommendation engine, that suggests if you look at the sweater maybe like that one, the risk of getting that wrong. And so fully automating that as a little bit lower, because the risk is you don't buy the sweater. I lose a little bit of income I lose a little bit of revenue as a retailer, but the risk of I make that turn, because I'm going to autonomous vehicle as much higher. So I think that you will see the progression up that curve being highly dependent on what's at stake, with different degrees of automation. That being said you will also see in certain places where there's, it's either really expensive or it's humans aren't doing a great job. You may actually start to see some mission critical automation. But those would be the places where you're seeing them. And actually I think that's one of the reasons why you see actually a lot more autonomy, in the agriculture space, than you do in the sort of passenger vehicle space. Because there's a lot at stake and it's very difficult for human beings to sort of drive large combines. >> plus they have a real they have a controlled environment. So I've interviewed Caterpillar they're doing a ton of stuff with autonomy. Cause they're there control that field, where those things are operating, and whether it's a field or a mine, it's actually fascinating how far they've come with autonomy. But let me switch to a different industry that I know is closer to your heart, and looking at some other interviews and let's talk about diagnosing disease. And if we take something specific like reviewing x-rays where the computer, and it also brings in the whole computer vision and bringing in computer vision algorithms, excuse me they can see things probably fast or do a lot more comparisons, than potentially a human doctor can. And or hopefully this whole signal to noise conversation elevate the signal for the doctor to review, and suppress the noise it's really not worth their time. They can also review a lot of literature, and hopefully bring a broader potential perspective of potential diagnoses within a set of symptoms. You said before you both your folks are physicians, and there's a certain kind of magic, a nuance, almost like kind of more childlike exploration to try to get out of the algorithm if you will to think outside the box. I wonder if you can share that, synergy between using computers and AI and machine learning to do really arduous nasty things, like going through lots and lots and lots and lots of, x-rays compared to and how that helps with, doctor who's got a whole different kind of set of experience a whole different kind of empathy, whole different type of relationship with that patient, than just a bunch of pictures of their heart or their lungs. >> I think that one of the things is, and this kind of goes back to this question of, is AI for decision support versus automation? And I think that what AI can do, and what we're pretty good at these days, with computer vision is picking up on subtle patterns right now especially if you have a very large data set. So if I can train on lots of pictures of lungs, it's a lot easier for me to identify the pictures that somehow these are not like the other ones. And that can be helpful but I think then to really interpret what you're seeing and understand is this. Is it actually bad quality image? Is it some kind of some kind of medical issue? And what is the medical issue? I think that's where bringing in, a lot of different types of knowledge, and a lot of different pieces of information. Right now I think humans are a little bit better at doing that. And some of that's because I don't think we have great ways to train on, sort of sparse datasets I guess. And the second part is that human beings might be 40 years of training a model. They 50 years of training a model as opposed to six months, or something with sparse information. That's another thing that human beings have their sort of lived experience, and the data that they bring to bear, on any type of prediction or classification is actually more than just say what they saw in their medical training. It might be the people they've met, the places they've lived what have you. And I think that's that part that sort of broader set of learning, and how things that might not be related might actually be related to your understanding of what you're looking at. I think we've got a ways to go from a sort of artificial intelligence perspective and developed. >> But it is Exascale day. And we all know about the compound exponential curves on the computing side. But let's shift gears a little bit. I know you're interested in emerging technology to support this effort, and there's so much going on in terms of, kind of the atomization of compute store and networking to be able to break it down into smaller, smaller pieces, so that you can really scale the amount of horsepower that you need to apply to a problem, to very big or to very small. Obviously the stuff that you work is more big than small. Work on GPU a lot of activity there. So I wonder if you could share, some of the emerging technologies that you're excited about to bring again more tools to the task. >> I mean, one of the areas I personally spend a lot of my time exploring are, I guess this word gets used a lot, the Cambrian  explosion of new AI accelerators. New types of chips that are really designed for different types of AI workloads. And as you sort of talked about going down, and it's almost in a way where we were sort of going back and looking at these large systems, but then exploring each little component on them, and trying to really optimize that or understand how that component contributes to the overall performance of the whole. And I think one of the things that just, I don't even know there's probably close to a hundred active vendors in the space of developing new processors, and new types of computer chips. I think one of the things that that points to is, we're moving in the direction of generally infrastructure heterogeneity. So it used to be when you built a system you probably had one type of processor, or you probably had a pretty uniform fabric across your system you usually had, I think maybe storage we started to get tearing a little bit earlier. But now I think that what we're going to see, and we're already starting to see it with Exascale systems where you've got GPUs and CPUs on the same blades, is we're starting to see as the workloads that are running at large scales are becoming more complicated. Maybe I'm doing some simulation and then I'm running I'm training some kind of AI model, and then I'm inferring it on some other type, some other output of the simulation. I need to have the ability to do a lot of different things, and do them in at a very advanced level. Which means I need very specialized technology to do it. And I think it's an exciting time. And I think we're going to test, we're going to break a lot of things. I probably shouldn't say that in this interview, but I'm hopeful that we're going to break some stuff. We're going to push all these systems to the limit, and find out where we actually need to push a little harder. And I some of the areas I think that we're going to see that, is there We're going to want to move data, and move data off of scientific instruments, into computing, into memory, into a lot of different places. And I'm really excited to see how it plays out, and what you can do and where the limits are of what you can do with the new systems. >> Arti I could talk to you all day. I love the experience and the perspective, cause you've been doing this for a long time. So I'm going to give you the final word before we sign out and really bring it back, to a more human thing which is ethics. So one of the conversations we hear all the time, is that if you are going to do something, if you're going to put together a project and you justify that project, and then you go and you collect the data and you run that algorithm and you do that project. That's great but there's like an inherent problem with, kind of data collection that may be used for something else down the road that maybe you don't even anticipate. So I just wonder if you can share, kind of top level kind of ethical take on how data scientists specifically, and then ultimately more business practitioners and other people that don't carry that title. Need to be thinking about ethics and not just kind of forget about it. That these are I had a great interview with Paul Doherty. Everybody's data is not just their data, it's it represents a person, It's a representation of what they do and how they lives. So when you think about kind of entering into a project and getting started, what do you think about in terms of the ethical considerations and how should people be cautious that they don't go places that they probably shouldn't go? >> I think that's a great question out a short answer. But I think that I honestly don't know that we have a great solutions right now, but I think that the best we can do is take a very multifaceted, and also vigilant approach to it. So when you're collecting data, and often we should remember a lot of the data that gets used isn't necessarily collected for the purpose it's being used, because we might be looking at old medical records, or old any kind of transactional records whether it be from a government or a business. And so as you start to collect data or build solutions, try to think through who are all the people who might use it? And what are the possible ways in which it could be misused? And also I encourage people to think backwards. What were the biases in place that when the data were collected, you see this a lot in the criminal justice space is the historical records reflect, historical biases in our systems. And so is I there are limits to how much you can correct for previous biases, but there are some ways to do it, but you can't do it if you're not thinking about it. So I think, sort of at the outset of developing solutions, that's important but I think equally important is putting in the systems to maintain the vigilance around it. So one don't move to autonomy before you know, what potential new errors you might or new biases you might introduce into the world. And also have systems in place to constantly ask these questions. Am I perpetuating things I don't want to perpetuate? Or how can I correct for them? And be willing to scrap your system and start from scratch if you need to. >> Well Arti thank you. Thank you so much for your time. Like I said I could talk to you for days and days and days. I love the perspective and the insight and the thoughtfulness. So thank you for sharing your thoughts, as we celebrate Exascale day. >> Thank you for having me. >> My pleasure thank you. All right she's Arti I'm Jeff it's Exascale day. We're covering on the queue thanks for watching. We'll see you next time. (bright upbeat music)

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

(plane engine roars) (upbeat music) >> They call me Dr. Goh. I'm Senior Vice President and Chief Technology Officer of AI at Hewlett Packard Enterprise. And today I'm in Munich, Germany. Home to one and a half million people. Munich is famous for everything from BMW, to beer, to breathtaking architecture and festive markets. The Bavarian capital is the beating heart of Germany's automobile industry. Over 50,000 of its residents work in automotive engineering, and to date, Munich allocated around 30 million euros to boost electric vehicles and infrastructure for them. (upbeat music) >> Hello, everyone, my name is Dr. Jerome Baudry. I am a professor at the University of Alabama in Huntsville. Our mission is to use a computational resources to accelerate the discovery of drugs that will be useful and efficient against the COVID-19 virus. On the one hand, there is this terrible crisis. And on the other hand, there is this absolutely unique and rare global effort to fight it. And that I think is a is a very positive thing. I am working with the Cray HPE machine called Sentinel. This machine is so amazing that it can actually mimic the screening of hundreds of thousands, almost millions of chemicals a day. What we take weeks, if not months, or years, we can do in a matter of a few days. And it's really the key to accelerating the discovery of new drugs, new pharmaceuticals. We are all in this together, thank you. (upbeat music) >> Hello, everyone. I'm so pleased to be here to interview Dr. Jerome Baudry, of the University of Alabama in Huntsville. >> Hello, Dr. Goh, I'm very happy to be meeting with you here, today. I have a lot of questions for you as well. And I'm looking forward to this conversation between us. >> Yes, yes, and I've got lots of COVID-19 and computational science questions lined up for you too Jerome. Yeah, so let's interview each other, then. >> Absolutely, let's do that, let's interview each other. I've got many questions for you. And , we have a lot in common and yet a lot of things we are addressing from a different point of view. So I'm very much looking forward to your ideas and insights. >> Yeah, especially now, with COVID-19, many of us will have to pivot a lot of our research and development work, to address the most current issues. I watch your video and I've seen that you're very much focused on drug discovery using super computing. The central notebook you did, I'm very excited about that. Can you tell us a bit more about how that works, yeah? >> Yes, I'd be happy to in fact, I watch your video as well manufacturing, and it's actually quite surprisingly close, what we do with drugs, and with what other people do with planes or cars or assembly lanes. we are calculating forces, on molecules, on drug candidates, when they hit parts of the viruses. And we essentially try to identify what small molecules will hit the viruses or its components, the hardest to mess with its function in a way. And that's not very different from what you're doing. What you are describing people in the industry or in the transportation industry are doing. So that's our problem, so to speak, is to deal with a lot of small molecules. Guy creating a lot of forces. That's not a main problem, our main problem is to make intelligent choices about what calculates, what kind of data should we incorporate in our calculations? And what kind of data should we give to the people who are going to do the testing? And that's really something I would like you to do to help us understand better. How do you see artificial intelligence, helping us, putting our hands on the right data to start with, in order to produce the right data and accuracy. >> Yeah, that's that's a great question. And it is a question that we've been pondering in our strategy as a company a lot recently. Because more and more now we realize that the data is being generated at the far out edge. By edge. I mean, something that's outside of the cloud and data center, right? Like, for example, a more recent COVID-19 work, doing a lot of cryo electron microscope work, right? To try and get high resolution pictures of the virus and at different angles, so creating lots of movies under electron microscope to try and create a 3D model of the virus. And we realize that's the edge, right, because that's where the microscope is, away from the data center. And massive amounts of data is generated, terabytes and terabytes of data per day generated. And we had to develop means, a workflow means to get that data off the microscope and provide pre-processing and processing, so that they can achieve results without delay. So we learned quite a few lessons there, right, especially trying to get the edge to be more intelligent, to deal with the onslaught of data coming in, from these devices. >> That's fantastic that you're saying that and that you're using this very example of cryo-EM, because that's the kind of data that feeds our computations. And indeed, we have found that it is very, very difficult to get the right cryo-EM data to us. Now we've been working with HPE supercomputer Sentinel, as you may know, for our COVID-19 work. So we have a lot of computational power. But we will be even faster and better, frankly, if we knew what kind of cryo-EM data to focus on. In fact, most of our discussions are based on not so much how to compute the forces of the molecules, which we do quite well on an HP supercomputer. But again, what cryo-EM 3D dimensional space to look at. And it's becoming almost a bottleneck. >> Have access to that. >> And we spend a lot of time, do you envision a point where AI will be able to help us, to make this kind of code almost live or at least as close to live as possible, as that that comes from the edge? How to pack it and not triage it, but prioritize it for the best possible computations on supercomputers? >> What a visionary question and desire, right? Like exactly the vision we have, right? Of course, the ultimate vision, you aim for the best, and that will be a real time stream of processed data coming off the microscope straight, providing your need, right? We are not there. Before this, we are far from there, right? But that's the aim, the ability to push more and more intelligence forward, so that by the time the data reaches you, it is what you need, right, without any further processing. And a lot of AI is applied there, particularly in cryo-EM where they do particle picking, right, they do a lot of active pictures and movies of the virus. And then what they do is, they rotate the virus a little bit, right? And then to try and figure out in all the different images in the movies, to try and pick the particles in there. And this is very much image processing that AI is very good at. So many different stages, application is made. The key thing, is to deal with the data that is flowing at this at this speed, and to get the data to you in the right form, that in time. So yes, that's the desire, right? >> It will be a game changer, really. You'll be able to get things in a matter of weeks, instead of a matter of years to the colleague who will be doing the best day. If the AI can help me learn from a calculation that didn't exactly turn out the way we want it to be, that will be very, very helpful. I can see, I can envision AI being able to, live AI to be able to really revolutionize all the process, not only from the discovery, but all the way to the clinical, to the patient, to the hospital. >> Well, that's a great point. In fact, I caught on to your term live AI. That's actually what we are trying to achieve. Although I have not used that term before. Perhaps I'll borrow it for next time. >> Oh please, by all means. >> You see, yes, we have done, I've been doing also recent work on gene expression data. So a vaccine, clinical trial, they have the blood, they get the blood from the volunteers after the first day. And then to run very, very fast AI analytics on the gene expression data that the one, the transcription data, before translation to emit amino acid. The transcription data is enormous. We're talking 30,000, 60,000 different items, transcripts, and how to use that high dimensional data to predict on day one, whether this volunteer will get an adverse event or will have a good antibody outcome, right? For efficacy. So yes, how to do it so quickly, right? To get the blood, go through an SA, right, get the transcript, and then run the analytics and AI to produce an outcome. So that's exactly what we're trying to achieve, yeah. Yes, I always emphasize that, ultimately, the doctor makes that decision. Yeah, AI only suggests based on the data, this is the likely outcome based on all the previous data that the machine has learned from, yeah. >> Oh, I agree, we wouldn't want the machine to decide the fate of the patient, but to assist the doctor or nurse making the decision that will be invaluable? And are you aware of any kind of industry that already is using this kind of live AI? And then, is there anything in, I don't know in sport or crowd control? Or is there any kind of industry? I will be curious to see who is ahead of us in terms of making this kind of a minute based decisions using AI? Yes, in fact, this is very pertinent question. We as In fact, COVID-19, lots of effort working on it, right? But now, industries and different countries are starting to work on returning to work, right, returning to their offices, returning to the factories, returning to the manufacturing plants, but yet, the employers need to reassure the employees that things, appropriate measures are taken for safety, but yet maintain privacy, right? So our Aruba organization actually developed a solution called contact location tracing inside buildings, inside factories, right? Why they built this, and needed a lot of machine learning methods in there to do very, very well, as you say, live AI right? To offer a solution? Well, let me describe the problem. The problem is, in certain countries, and certain states, certain cities where regulations require that, if someone is ill, right, you actually have to go in and disinfect the area person has been to, is a requirement. But if you don't know precisely where the ill person has been to, you actually disinfect the whole factory. And if you have that, if you do that, it becomes impractical and cost prohibitive for the company to keep operating profitably. So what they are doing today with Aruba is, that they carry this Bluetooth Low Energy tag, which is a quarter size, right? The reason they do that is, so that they extract the tag from the person, and then the system tracks, everybody, all the employees. We have one company, there's 10,000 employees, right? Tracks everybody with the tag. And if there is a person ill, immediately a floor plan is brought up with hotspots. And then you just targeted the cleaning services there. The same thing, contact tracing is also produced automatically, you could say, anybody that is come in contact with this person within two meters, and more than 15 minutes, right? It comes up the list. And we, privacy is our focused here. There's a separation between the tech and the person, on only restricted people are allowed to see the association. And then things like washrooms and all that are not tracked here. So yes, live AI, trying to make very, very quick decisions, right, because this affects people. >> Another question I have for you, if you have a minute, actually has to be the same thing. Though, it's more a question about hardware, about computer hardware purify may. We're having, we're spending a lot of time computing on number crunching giant machines, like Sentinel, for instance, which is a dream to use, but it's very good at something but when we pulled it off, also spent a lot of time moving back and forth, so data from clouds from storage, from AI processing, to the computing cycles back and forth, back and forth, did you envision an architecture, that will kind of, combine the hardware needed for a massively parallel calculations, kind of we are doing. And also very large storage, fast IO to be more AI friendly, so to speak. You see on the horizon, some kind of, I would say you need some machine, maybe it's to be determined, to be ambitious at times but something that, when the AI ahead plan in terms of passing the vector to the massively parallel side, yeah, that makes sense? >> Makes a lot of sense. And you ask it I know, because it is a tough problem to solve, as we always say, computation, right, is growing capability enormously. But bandwidth, you have to pay for, latency you sweat for, right? >> That's a very good >> So moving data is ultimately going to be the problem. >> It is. >> Yeah, and we've move the data a lot of times, right, >> You move back and forth, so many times >> Back and forth, back and forth, from the edge that's where you try to pre-process it, before you put it in storage, yeah. But then once it arrives in storage, you move it to memory to do some work and bring it back and move it memory again, right, and then that's what HPC, and then you put it back into storage, and then the AI comes in you, you do the learning, the other way around also. So lots of back and forth, right. So tough problem to solve. But more and more, we are looking at a new architecture, right? Currently, this architecture was built for the AI side first, but we're now looking and see how we can expand that. And this is that's the reason why we announced HPE Ezmeral Data Fabric. What it does is that, it takes care of the data, all the way from the edge point of view, the minute it is ingested at the edge, it is incorporated in the global namespace. So that eventually where the data arrives, lands at geographically one, or lands at, temperature, hot data, warm data or cold data, regardless of eventually where it lands at, this Data Fabric checks everything, from in a global namespace, in a unified way. So that's the first step. So that data is not seen as in different places, different pieces, it is a unified view of all the data, the minute that it does, Just start from the edge. >> I think it's important that we communicate that AI is purposed for good, A lot of sci-fi movies, unfortunately, showcase some psychotic computers or teams of evil scientists who want to take over the world. But how can we communicate better that it's a tool for a change, a tool for good? >> So key differences are I always point out is that, at least we have still judgment relative to the machine. And part of the reason we still have judgment is because our brain, logical center is automatically connected to our emotional center. So whatever our logic say is tempered by emotion, and whatever our emotion wants to act, wants to do, right, is tempered by our logic, right? But then AI machine is, many call them, artificial specific intelligence. They are just focused on that decision making and are not connected to other more culturally sensitive or emotionally sensitive type networks. They are focus networks. Although there are people trying to build them, right. That's this power, reason why with judgment, I always use the phrase, right, what's correct, is not always the right thing to do. There is a difference, right? We need to be there to be the last Judge of what's right, right? >> Yeah. >> So that says one of the the big thing, the other one, I bring up is that humans are different from machines, generally, in a sense that, we are highly subtractive. We, filter, right? Well, machine is highly accumulative today. So an AI machine they accumulate to bring in lots of data and tune the network, but our brains a few people realize, we've been working with brain researchers in our work, right? Between three and 30 years old, our brain actually goes through a pruning process of our connections. So for those of us like me after 30 it's done right. (laughs) >> Wait till you reach my age. >> Keep the brain active, because it prunes away connections you don't use, to try and conserve energy, right? I always say, remind our engineers about this point, about prunings because of energy efficiency, right? A slice of pizza drives our brain for three hours. (laughs) That's why, sometimes when I get need to get my engineers to work longer, I just offer them pizza, three more hours, >> Pizza is universal solution to our problems, absolutely. Food Indeed, indeed. There is always a need for a human consciousness. It's not just a logic, it's not like Mr. Spock in "Star Trek," who always speaks about logic but forgets the humanity aspect of it. >> Yes, yes, The connection between the the logic centers and emotional centers, >> You said it very well. Yeah, yeah and the thing is, sleep researchers are saying that when you don't get enough REM sleep, this connection is weakened. Therefore, therefore your decision making gets affected if you don't get enough sleep. So I was thinking, people do alcohol test breathalyzer test before they are allowed to operate sensitive or make sensitive decisions. Perhaps in the future, you have to check whether you have enough REM sleep before, >> It is. This COVID-19 crisis obviously problematic, and I wish it never happened, but there is something that I never experienced before is, how people are talking to each other, people like you and me, we have a lot in common. But I hear more about the industry outside of my field. And I talk a lot to people, like cryo-EM people or gene expression people, I would have gotten the data before and process it. Now, we have a dialogue across the board in all aspects of industry, science, and society. And I think that could be something wonderful that we should keep after we finally fix this bug. >> Yes. yes, yes. >> Right? >> Yes, that's that's a great point. In fact, it's something I've been thinking about, right, for employees, things have changed, because of COVID-19. But very likely, the change will continue, yeah? >> Right. Yes, yes, because there are a few positive outcomes. COVID-19 is a tough outcome. But there positive side of things, like communicating in this way, effectively. So we were part of the consortium that developed a natural language processing system in AI system that would allow you scientists to do, I can say, with the link to that website, allows you to do a query. So say, tell me the latest on the binding energy between the Sasko B2 virus like protein and the AC receptor. And then you will, it will give you a list of 10 answers, yeah? And give you a link to the papers that say, they say those answers. If you key that in today to NLP, you see 315 points -13.7 kcal per mole, which is right, I think the general consensus answer, and see a few that are highly out of out of range, right? And then when you go further, you realize those are the earlier papers. So I think this NLP system will be useful. (both chattering) I'm sorry, I didn't mean to interrupt, but I mentioned yesterday about it, because I have used that, and it's a game changer indeed, it is amazing, indeed. Many times by using this kind of intelligent conceptual, analyzes a very direct use, that indeed you guys are developing, I have found connections between facts, between clinical or pharmaceutical aspects of COVID-19. That I wasn't really aware of. So a it's a tool for creativity as well, I find it, it builds something. It just doesn't analyze what has been done, but it creates the connections, it creates a network of knowledge and intelligence. >> That's why three to 30 years old, when it stops pruning. >> I know, I know. (laughs) But our children are amazing, in that respect, they see things that we don't see anymore. they make connections that we don't necessarily think of, because we're used to seeing a certain way. And the eyes of a child, are bringing always something new, which I think is what AI could potentially bring here. So look, this is fascinating, really. >> Yes, yes, difference between filtering subtractive and the machine being accumulative. That's why I believe, the two working together, can have a stronger outcome if used properly. >> Absolutely. And I think that's how AI will be a force for good indeed. Obviously see, seems that we would have missed that would end up being very important. Well, we are very interested in or in our quest for drug discovery against COVID-19, we have been quite successful so far. We have accelerated the process by an order of magnitude. So we're having molecules that are being tested against the virus, otherwise, it would have taken maybe three or four years to get to that point. So first thing, we have been very fast. But we are very interested in natural products, that chemicals that come from plants, essentially. We found a way to mine, I don't want to say explore it, but leverage, that knowledge of hundreds of years of people documenting in a very historical way of what plants do against what diseases in different parts of the world. So that really has been a, not only very useful in our work, but a fantastic bridge to our common human history, basically. And second, yes, plants have chemicals. And of course we love chemicals. Every living cell has chemicals. The chemicals that are in plants, have been fine tuned by evolution to actually have some biological function. They are not there just to look good. They have a role in the cell. And if we're trying to come up with a new growth from scratch, which is also something we want to do, of course, then we have to engineer a function that evolution hasn't already found a solution to, for in plants, so in a way, it's also artificial intelligence. We have natural solutions to our problems, why don't we try to find them and see their work in ourselves, we're going to, and this is certainly have to reinvent the wheel each time. >> Hundreds of millions of years of evolution, >> Hundreds of millions of years. >> Many iterations, >> Yes, ending millions of different plants with all kinds of chemical diversity. So we have a lot of that, at our disposal here. If only we find the right way to analyze them, and bring them to our supercomputers, then we will, we will really leverage this humongus amount of knowledge. Instead of having to reinvent the wheel each time we want to take a car, we'll find that there are cars whose wheels already that we should be borrowing instead of, building one each time. Most of the keys are out there, if we can find them, They' re at our disposal. >> Yeah, nature has done the work after hundreds of millions of years. >> Yes. (chattering) Is to figure out, which is it, yeah? Exactly, exactly hence the importance of biodiversity. >> Yeah, I think this is related to the Knowledge Graph, right? Where, yes, to objects and the linking parameter, right? And then you have hundreds of millions of these right? A chemical to an outcome and the link to it, right? >> Yes, that's exactly what it is, absolutely the kind of things we're pursuing very much, so absolutely. >> Not only only building the graph, but building the dynamics of the graph, In the future, if you eat too much Creme Brulee, or if you don't run enough, or if you sleep, well, then your cells, will have different connections on this graph of the ages, will interact with that molecule in a different way than if you had more sleep or didn't eat that much Creme Brulee or exercise a bit more, >> So insightful, Dr. Baudry. Your, span of knowledge, right, impressed me. And it's such fascinating talking to you. (chattering) Hopefully next time, when we get together, we'll have a bit of Creme Brulee together. >> Yes, let's find out scientifically what it does, we have to do double blind and try three times to make sure we get the right statistics. >> Three phases, three clinical trial phases, right? >> It's been a pleasure talking to you. I like we agreed, you knows this, for all that COVID-19 problems, the way that people talk to each other is, I think the things that I want to keep in this in our post COVID-19 world. I appreciate very much your insight and it's very encouraging the way you see things. So let's make it happen. >> We will work together Dr.Baudry, hope to see you soon, in person. >> Indeed in person, yes. Thank you. >> Thank you, good talking to you.

>> Male Voice: From around the globe, it's theCUBE covering HPE Discover Virtual Experience brought to you by HPE. >> Hi everybody, you're watching theCUBE. And this is Dave Vellante in our continuous coverage of the Discover 2020 Virtual Experience, HPE's virtual event, theCUBE is here, theCUBE virtual. We're really excited, we got a great session here. We're going to dig deep into machine intelligence and artificial intelligence. Dr. Arti Garg is here. She's the Head of Advanced AI Solutions and Technologies at Hewlett Packard Enterprise. And she's joined by Dr. Sorin Cheran, who is the Vice President of AI Strategy and Solutions Group at HPE. Folks, great to see you. Welcome to theCUBE. >> Hi. >> Hi, nice to meet you, hello! >> Dr. Cheran, let's start with you. Maybe talk a little bit about your role. You've had a variety of roles and maybe what's your current situation at HPE? >> Hello! Hi, so currently at HPE, I'm driving the Artificial Intelligence Strategy and Solution group who is currently looking at how do we bring solutions across the HPE portfolio, looking at every business unit, but also on the various geos. At the same time, the team is responsible for building the strategy around the AI for the entire company. We're working closely with the field, we're working closely with the things that are facing the customers every day. And we're also working very closely with the various groups in order to make sure that whatever we build holds water for the entire company. >> Dr. Garg, maybe you could share with us your focus these days? >> Yeah, sure, so I'm also part of the AI Strategy and Solutions team under Sorin as our new vice president in that role, and what I'm focused on is really trying to understand, what are some of the emerging technologies, whether those be things like new processor architectures, or advanced software technologies that could really enhance what we can offer to our customers in terms of AI and exploring what makes sense and how do we bring them to our customers? What are the right ways to package them into solutions? >> So everybody's talking about how digital transformation has been accelerated. If you're not digital, you can't transact business. AI infused into every application. And now people are realizing, "Hey, we can't solve all the world's problems with labor." What are you seeing just in terms of AI being accelerated throughout the portfolio and your customers? >> So that's a very good idea, because we've been talking about digital transformation for some time now. And I believe most of our customers believed initially that the one thing they have is time thinking that, "Oh yes I'm going to somehow at one point apply AI "and somehow at one point "I'm going to figure out how to build the data strategy, "or how to use AI in my different line of businesses." What happened with COVID-19 and in this area is that we lost one thing: time. So I think discussed what they see in our customers is the idea of accelerating their data strategy accelerating, moving from let's say an environment where they would compute center models per data center models trying to understand how do they capture data, how they accelerate the adoption of AI within the various business units, why? Because they understand that currently the way they are actually going to the business changed completely, they need to understand how to adapt a new business model, they need to understand how to look for value pools where there are none as well. So most of our customers today, while initially they spend a lot of time in an never ending POC trying to investigate where do they want to go. Currently they do want to accelerate the application of AI models, the build of data strategies, how then they use all of this data? How do they capture the data to make sure that they look at new business models, new value pools, new customer experience and so on and so forth. So I think what they've seen in the past, let's say three to six months is that we lost time. But the shift towards an adoption of analytics, AI and data strategy is accelerated a lot, simply because customers realize that they need to get ahead of the game. >> So Dr. Garg, what if you could talk about how HPE is utilizing machine intelligence during this pandemic, maybe helping some of your customers, get ahead of it, or at least trying to track it. How are you applying AI in this context? >> So I think that Sorin sort of spoke to one of the things with adopting AI is, it's very transformational for a business so it changes how you do things. You need to actually adopt new processes to take advantage of it. So what I would say is right now we're hearing from customers who recognize that the context in which they are doing their work is completely different. And they're exploring how AI can help them really meet the challenges of those context. So one example might be how can AI and computer vision be coupled together in a way that makes it easier to reopen stores, or ensures that people are distancing appropriately in factories. So I would say that it's the beginning of these conversations as customers as businesses try to figure out how do we operate in the new reality that we have? And I think it's a pretty exciting time. And I think just to the point that Sorin just made, there's a lot of openness to new technologies that there wasn't before, because there's this willingness to change the business processes to really take advantage of any technologies. >> So Dr. Cheran, I probably should have started here but help us understand HPE's overall strategy with regard to AI. I would certainly know that you're using AI to improve IT, the InfoSite product and capability via the Nimble acquisition, et cetera, and bringing that across the portfolio. But what's the strategy for HPE? >> So, yeah, thank you. That's (laughs) a good question. So obviously you started with a couple of our acquisition in the past because obviously Nimble and then we talked a lot about our efforts to bring InfoSite across the portfolio. But currently, in the past couple of months, let's say close to a year, we've been announcing a lot of other acquisitions and we've been talking about Tuteybens, we've been talking about Scytale we've been talking about Cray, and so on, so forth, and now what we're doing at HPE is to bring all of this IP together into one place and try to help our customers within their region out. If you're looking at what, for example, what did they actually get when Cray play was not only the receiver, but we also acquire and they also have a lot of software and a lot of IP around optimization and so on and so forth. Also within our own labs, we've been investigating AI around like, for example, some learning or accelerators or a lot of other activity. So right now what we're trying to help our customers with is to understand how do they lead from the production stage, from the POC stage to the production stage. So (mumbles) what we are trying to do is we are trying to accelerate their adoption of AI. So simply starting from an optimized platform infrastructure up to the solution they are actually going to apply or to use to solve their business problems and wrapping all of that around with services either consumed on-prem as a service and so on. So practically what we want to do is we want to help our customers optimize, orchestrate and operationalize AI. Because the problem of our customers is not to start in our PLC, the problem is how do I then take everything that I've been developing or working on and then put it in production at the edge, right? And then keep it, maintaining production in order to get insights and then actually take actions that are helping the enterprise. So basically, we want to be data driven assets in cloud enable, and we want to help our customers move from POC into production. >> Or do you work with obviously a lot of data folks, companies or data driven data scientists, you are hands on practitioners in this regard. One of the challenges that I hear a lot from customers is they're trying to operationalize AI put AI into production, they have data in silos, they spend all their time, munging data, you guys have made a number of acquisitions. Not a list of which is prey, obviously map of, data specialist, my friend Kumar's company Blue Data. So what do you see as HPE's role in terms of helping companies operationalize AI. >> So I think that a big part of operationalizing AI moving away from the PLC to really integrate AI into the business processes you have and also the sort of pre existing IT infrastructure you talked about, you might already have siloed data. That's sort of something we know very well at HPE, we understand a lot of the IT that enterprises already have the incumbent IT and those systems. We also understand how to put together systems and integrated systems that include a lot of different types of computing infrastructure. So whether that being different types of servers and different types of storage, we have the ability to bring all of that together. And then we also have the software that allows you to talk to all of these different components and build applications that can be deployed in the real world in a way that's easy to maintain, and scale and grow as your AI applications will almost invariably get more complex involved, more outputs involved and more input. So one of the important things as customers try to operationalize AI is think is knowing that it's not just solving the problem you're currently solving. It's not just operationalizing the solution you have today, it's ensuring that you can continue to operationalize new things or additional capabilities in the future. >> I want to talk a little bit about AI for good. We talked about AI taking away jobs, but the reality is, when you look at the productivity data, for instance, in the United States, in Europe, it's declining and it has for the last several decades and so I guess my point is that we're not going to be able to solve some of the world problems in the coming decades without machine intelligence. I mean you think about health care, you think about feeding populations, you think about obviously paying things like pandemics, climate change, energy alternatives, et cetera, productivity is coming down. Machines are potential opportunity. So there's an automation imperative. And you feel, Dr. Cheran, the people who are sort of beyond that machines replacing human's issue? Is that's still an item or has the pandemic sort of changed that? >> So I believe it is, so it used to be a very big item, you're right. And every time we were speaking at a conference and every time you're actually looking at the features of AI, right? Two scenarios are coming to plays, right? The first one where machines are here, actually take a walk, and then the second one as you know even a darker version where terminator is coming, yes and so forth, right? So basically these are the two, is the lesser evil in the greater evil and so on and so forth. And we still see that regular thing coming over and over again. And I believe that 2019 was the year of reckoning, where people are trying to realize that not only we can actually take responsible AI, but we can actually create an AI that is trustworthy, an AI that is fair and so on and so forth. And that we also understood in 2019 it was highly debated everywhere, which part of our jobs are going to be replaced like the parts that are mundane, or that can actually be easily automated and so on and so forth. With the COVID-19 what happened is that people are starting to look at AI differently, why? Because people are starting to look at data differently. And looking at data differently, how do I actually create this core of data which is trusted, secure and so on and so forth, and they are trying to understand that if the data is trusted and secure somehow, AI will be trusted and secure as well. Now, if I actually shifted forward, as you said, and then I try to understand, for example on the manufacturing floor, how do I add more machines? Or how do I replace humans with machines simply because, I need to make sure that I am able to stay in production and so on and so forth. From their perspective, I don't believe that the view of all people are actually looking at AI from the job marketplace perspective changed a lot. The view that actually changes how AI is helping us better certain prices, how AI is helping us, for example, in health care, but the idea of AI actually taking part of the jobs or automating parts of the jobs, we are not actually past yet, even if 2018 and even more so in 2019, it was the year also where actually AI through automation replaced the number of jobs but at the same time because as I was saying the first year where AI created more jobs it's because once you're displacing in one place, they're actually creating more work more opportunities in other places as well. But still, I don't believe the feeling changed. But we realize that AI is a lot more valuable and it can actually help us through some of our darkest hours, but also allow us to get better and faster insights as well. >> Well, machines have always replaced humans and now for the first time in history doing so in a really cognitive functions in a big way. But I want to ask you guys, I'll start with Dr. Arti, a series of questions that I think underscore the impact of AI and the central role that it plays in companies digital transformations, we talk about that a lot. But the questions that I'm going to ask you, I think will hit home just in terms of some hardcore examples, and if you have others I'd love to hear them but I'm going to start with Arti. So when do you think Dr. or machines will be able to make better diagnoses than doctors? We're actually there today already? >> So I think it depends a little bit on how you define that. And I'm just going to preface this by saying both of my parents are physicians. So I have a little bit of bias in this space. But I think that humans can bring creativity in a certain type of intelligence that it's not clear to me. We even know how to model with the computer. And so diagnoses have sometimes two components. One is recognizing patterns and being able to say, "I'm going to diagnose this disease that I've seen before." I think that we are getting to the place where there are certain examples. It's just starting to happen where you might be able to take the data that you need to make a diagnosis as well understood. A machine may be able to sort of recognize those subtle patterns better. But there's another component of doing diagnosis is when it's not obvious what you're looking for. You're trying to figure out what is the actual sort of setup diseases I might be looking at. And I think that's where we don't really know how to model that type of inspiration and creativity that humans still bring to things that they do, including medical diagnoses. >> So Dr. Cheran my next question is, when do you think that owning and driving your own vehicle will become largely obsolete? >> (laughs) Well, I believe my son is six year old now. And I believe, I'm working with a lot of companies to make sure that he will not get his driving license with his ID, right? So depending who you're asking and depending the level of autonomy that you're looking at, but you just mentioned the level five most likely. So there are a lot of dates out there so some people actually say 2030. I believe that my son in most of the cities in US but also most of the cities in Europe, by the time he's 18 in let's say 2035, I'll try to make sure that I'm working with the right companies not to allow them to get the driving license. >> I'll let my next question is from maybe both of you can answer. Do you take the traditional banks will lose control of payment system? >> So that's an interesting question, because I think it's broader than an AI question, right? I think that it goes into some other emerging technologies, including distributed ledgers and sort of the more secure forms of blockchain. I think that's a challenging question to my mind, because it's bigger than the technology. It's got Economic and Policy implications that I'm not sure I can answer. >> Well, that's a great answer, 'cause I agree with you already. I think that governments and banks have a partnership. It's important partnership for social stability. But similar we've seen now, Dr. Cheran in retail, obviously the COVID-19 has affected retail in a major way, especially physical retail, do you think that large retail stores are going to go away? I mean, we've seen many in chapter 11. At this point, how much of that is machine intelligence versus just social change versus digital transformation? It's an interesting question, isn't it? >> So I think most of the... Right now the retailers are here to stay I guess for the next couple of years. But moving forward, I think their capacity of adapting to stores like to walk in stores or to stores where basically we just go in and there are no shop assistants and just you don't even need the credit card to pay you're actually being able to pay either with your face or with your phone or with your small chips and so on and so forth. So I believe currently in the next couple of years, obviously they are here to stay. Moving forward then we'll get artificial intelligence, or robotics applied everywhere in the store and so on and so forth. Most likely their capacity of adapting to the new normal, which is placing AI everywhere and optimizing the walk in through predicting when and how to guide the customers to the shop, and so on and so forth, would allow them to actually survive. I don't believe that everything is actually going to be done online, especially from the retailer perspective. Most of the... We've seen a big shift at COVID-19. But what I was reading the other day, especially in France that the counter has opened again, we've seen a very quick pickup in the retailers of people that actually visiting the stores as well. So it's going to be some very interesting five to 10 years, and then most of the companies that have adapted to the digital transformation and to the new normal I think they are here to stay. Some of them obviously are going to take sometime. >> I mean, I think it's an interesting question too that you really sort of triggering in my mind is when you think about the framework for how companies are going to come back and come out of this, it's not just digital, that's a big piece of it, like how digital businesses, can they physically distance? I mean, I don't know how sports arenas are going to be able to physically distance that's going to be interesting to see how essential is the business and if you think about the different industries that it really is quite different across those industries. And obviously, digital plays a big factor there, but maybe we could end on that your final thoughts and maybe any other other things you'd like to share with our audience? >> So I think one of the things that's interesting anytime you talk about adopting a new technology, and right now we're happening to see this sort of huge uptick in AI adoption happening right at the same time but this sort of massive shift in how we live our lives is happening and sort of an acceptance, I think that can't just go back to the way things work as you mentioned, they'll probably be continued sort of desire to maintain social distancing. I think that it's going to force us to sort of rethink why we do things the way we do now, a lot, the retail, environments that we have the transportation solutions that we have, they were adapted in many cases in a very different context, in terms of what people need to do on a day-to-day basis within their life. And then what were the sort of state of technologies available. We're sort of being thrust and forced to reckon with like, what is it I really need to do to live my life and then what are the technologies I have available to meet to answer that and I think, it's really difficult to predict right now what people will think is important about a retail experience, I wouldn't be surprised if you start to find in person retail actually be much less, technologically aided, and much more about having the ability to talk to a human being and get their opinion and maybe the tactile sense of being able to like touch new clothes, or whatever it is. And so it's really difficult I think right now to predict what things are going to look like maybe even a year or two from now from that perspective. I think that what I feel fairly confident is that people are really starting to understand and engage with new technologies, and they're going to be really open to thinking about what those new technologies enable them to do in this sort of new way of living that we're going to probably be entering pretty soon. >> Excellent! All right, Sorin, bring us home. We'll give you the last word on this topic. >> Now, so I wanted to... I agree with Arti because what these three months of staying at home and of busy shutting down allowed us to do was to actually have a very big reset. So let's say a great reset but basically we realize that all the things we've taken from granted like our freedom of movement, our technology, our interactions with each other, and also for suddenly we realize that everything needs to change. And the only one thing that we actually kept doing is interacting with each other remotely, interacting with each other with our peers in the house, and so on and so forth. But the one thing that stayed was generating data, and data was here to stay because we actually leave traces of data everywhere we go, we leave traces of data when we put our watch on where we are actually playing with our phone, or to consume digital and so on and so forth. So what these three months reinforced for me personally, but also for some of our customers was that the data is here to stay. And even if the world shut down for three months, we did not generate less data. Data was there on the contrary, in some cases, more data. So the data is the main enabler for the new normal, which is going to pick up and the data will actually allow us to understand how to increase customer experience in the new normal, most likely using AI. As I was saying at the beginning, how do I actually operate new business model? How do I find, who do I partner with? How do I actually go to market together? How do I make collaborations more secure, and so on and so forth. And finally, where do I actually find new value pools? For example, how do I actually still enjoy for having a beer in a pub, right? Because suddenly during the COVID-19, that wasn't possible. I have a very nice place around the corner, but it's actually cheaply stuff. I'm not talking about beer but in general, I mean, so the finance is different the pools of data, the pools (mumbles) actually, getting values are different as well. So data is here to stay, and the AI definitely is going to be accelerated because it needs to use data to allow us to adopt the new normal in the digital transformation. >> A lot of unknowns but certainly machines and data are going to play a big role in the coming decade. I want to thank Dr. Arti Garg and Dr. Sorin Cheran for coming on theCUBE. It's great to have you. Thank you for a wonderful conversation. Really appreciate it. >> Thank you very much. >> Thanks so much. >> All right. And thank you for watching everybody. This is Dave Vellante for theCUBE and the HPE 2020 Virtual Experience. We'll be right back right after this short break. (upbeat music)