(upbeat music) >> Hello, everyone; I'm John Furrier with SiliconANGLE NEWS and host of theCUBE, and welcome to our SiliconANGLE NEWS MWC NEWS UPDATE in Barcelona where MWC is the premier event for the cloud telecommunication industry, and in the news here is Red Hat, Red Hat announcing a collaboration with NVIDIA, Samsung and Arm on Efficient Open Networks. Red Hat announced updates across various fields including advanced 5G telecommunications cloud, industrial edge, artificial intelligence, and radio access networks, RAN, and Efficiency. Red Hat's enterprise Kubernetes platform, OpenShift, has added support for NVIDIA's converged accelerators and aerial SDK facilitating RAND deployments on industry standard service across hybrid and multicloud platforms. This composable infrastructure enables telecom firms to support heavier compute demands for edge computing, AI, private 5G, and more, and just also helps network operators adopt open architectures, allowing them to choose non-proprietary components from multiple suppliers. In addition to the NVIDIA collaboration, Red Hat is working with Samsung to offer a new vRAN solution for service providers to better manage their open RAN networks. They're also working with UK chip designer, Arm, to create new networking solutions for energy efficient Red Hat Open Source Kubernetes-based Efficient Power Level Exporter project, or Kepler, has been donated to the open Cloud Native Compute Foundation, allowing enterprise to better understand their cloud native workloads and power consumptions. Kepler can also help in the development of sustainable software by creating less power hungry applications. Again, Red Hat continuing to provide OpenSource, OpenRAN, and contributing an open source project to the CNCF, continuing to create innovation for developers, and, of course, Red Hat knows what, a lot about operating systems and the telco could be the next frontier. That's SiliconANGLE NEWS. I'm John Furrier; thanks for watching. (monotone music)

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

(upbeat music) >> Let's kick things off. We're here at Mike Capuano the CMO of Pluribus Networks, and Ami Badani VP of Networking, Marketing, and Developer of Ecosystem at NVIDIA. Great to have you welcome folks. >> Thank you. >> Thanks. >> So let's get into the the problem situation with cloud unified networking. What problems are out there? What challenges do cloud operators have Mike? Let's get into it. >> The challenges that we're looking at are for non hyperscalers that's enterprises, governments Tier 2 service providers, cloud service providers. And the first mandate for them is to become as agile as a hyperscaler. So they need to be able to deploy services and security policies in seconds. They need to be able to abstract the complexity of the network and define things in software while it's accelerated in hardware. Really ultimately they need a single operating model everywhere. And then the second thing is they need to distribute networking and security services out to the edge of the host. We're seeing a growth cyber attacks. It's not slowing down. It's only getting worse and solving for this security problem across clouds is absolutely critical. And the way to do it is to move security out to the host. >> With that goal in mind, what's the Pluribus vision how does this tie together? >> So basically what we see is that this demands a new architecture and that new architecture has four tenets. The first tenet is unified and simplified cloud networks. If you look at cloud networks today, there's sort of like discreet bespoke cloud networks per hypervisor, per private cloud, edge cloud, public cloud. Each of the public clouds have different networks, that needs to be unified. If we want these folks to be able to be agile they need to be able to issue a single command or instantiate a security policy across all of those locations with one command and not have to go to each one. The second is, like I mentioned distributed security. Distributed security without compromise, extended out to the host is absolutely critical. So micro segmentation and distributed firewalls. But it doesn't stop there. They also need pervasive visibility. It's sort of like with security you really can't see you can't protect you can't see. So you need visibility everywhere. The problem is visibility to date has been very expensive. Folks have had to basically build a separate overlay network of taps, packet brokers, tap aggregation infrastructure, that really needs to be built in to this unified network I'm talking about. And the last thing is automation. All of this needs to be SDN enabled. So this is related to my comment about abstraction. Abstract the complexity of all these discreet networks whatever's down there in the physical layer. I don't want to see it. I want to abstract it. I want to define things in software but I do want to leverage the power of hardware to accelerate that. So that's the fourth tenet is SDN automation. >> Mike, we've been talking on theCUBE a lot about this architectural shift and customers are looking at this. This is a big part of everyone who's looking at cloud operations, NextGen. How do we get there? How do customer customers get this vision realized? >> That's a great question. And I appreciate the tee up. We're here today for that reason. We're introducing two things today. The first is a unified cloud networking vision. And that is a vision of where Pluribus is headed with our partners like NVIDIA long term. And that is about deploying a common operating model SDN enabled, SDN automated, hardware accelerated across all clouds. And whether that's underlay and overlay switch or server, any hypervisor infrastructure containers, any workload doesn't matter. So that's ultimately where we want to get. And that's what we talked about earlier. The first step in that vision is what we call the unified cloud fabric. And this is the next generation of our adaptive cloud fabric. And what's nice about this is we're not starting from scratch. We have an award-winning adaptive cloud fabric product that is deployed globally. And in particular, we're very proud of the fact that it's deployed in over 100 Tier 1 mobile operators as the network fabric for their 4G and 5G virtualized cores. We know how to build carrier grade networking infrastructure. What we're doing now to realize this next generation unified cloud fabric is we're extending from the switch to this NVIDIA BlueField-2 DPU. We know there's. >> Hold that up real quick. That's a good prop. That's the BlueField NVIDIA card. >> It's the NVIDIA BlueField-2 DPU, data processing unit. What we're doing fundamentally is extending our SDN automated fabric, the unified cloud fabric, out to the host. But it does take processing power. So we knew that we didn't want to do we didn't want to implement that running on the CPUs which is what some other companies do. Because it consumes revenue generating CPUs from the application. So a DPU is a perfect way to implement this. And we knew that NVIDIA was the leader with this BlueField-2. And so that is the first, that's the first step into getting, into realizing this vision. >> NVIDIA has always been powering some great workloads of GPUs, now you got DPUs. Networking and NVIDIA as here. What is the relationship with Pluribus? How did that come together? Tell us the story. >> We've been working with Pluribus for quite some time. I think the last several months was really when it came to fruition. And what Pluribus is trying to build and what NVIDIA has. So we have, this concept of a blue field data processing unit, which, if you think about it, conceptually does really three things, offload, accelerate, and isolate. So offload your workloads from your CPU to your data processing unit, infrastructure workloads that is. Accelerate, so there's a bunch of acceleration engines. You can run infrastructure workloads much faster than you would otherwise. And then isolation, So you have this nice security isolation between the data processing unit and your other CPU environment. And so you can run completely isolated workloads directly on the data processing unit. So we introduced this, a couple years ago. And with Pluribus we've been talking to the Pluribus team for quite some months now. And I think really the combination of what Pluribus is trying to build, and what they've developed around this unified cloud fabric fits really nicely with the DPU and running that on the DPU and extending it really from your physical switch all the way to your host environment, specifically on the data processing unit. So if you think about what's happening as you add data processing units to your environment. So every server we believe over time is going to have data processing units. So now you'll have to manage that complexity from the physical network layer to the host layer. And so what Pluribus is really trying to do is extending the network fabric from the host from the switch to the host and really have that single pane of glass for network operators to be able to configure, provision, manage all of the complexity of the network environment. So that's really how the partnership truly started. And so it started really with extending the network fabric and now we're also working with them on security. If you sort of take that concept of isolation and security isolation, what Pluribus has within their fabric is the concept of micro segmentation. And so now you can take that extend it to the data processing unit and really have isolated micro segmentation workloads whether it's bare metal, cloud native environments, whether it's virtualized environments, whether it's public cloud, private cloud, hybrid cloud. So it really is a magical partnership between the two companies with their unified cloud fabric running on the DPU. >> You know what I love about this conversation is it reminds me of when you have these changing markets. The product gets pulled out of the market and you guys step up and create these new solutions. And I think this is a great example. So I have to ask you how do you guys differentiate what sets this apart for customers? What's in it for the customer? >> So I mentioned three things in terms of the value of what the BlueField brings. There's offloading, accelerating and isolating. And that's sort of the key core tenets of BlueField. So that, if you sort of think about what BlueField what we've done, in terms of the differentiation. We're really a robust platform for innovation. So we introduced BlueField-2 last year. We're introducing BlueField-3 which is our next generation of blue field. It'll have 5X the ARM compute capacity. It will have 400 gig line rate acceleration, 4X better crypto acceleration. So it will be remarkably better than the previous generation. And we'll continue to innovate and add, chips to our portfolio every 18 months to two years. So that's sort of one of the key areas of differentiation. The other is that if you look at NVIDIA, what we're sort of known for is really known for our AI, our artificial intelligence and our artificial intelligence software, as well as our GPU. So you look at artificial intelligence and the combination of artificial intelligence plus data processing. This really creates faster, more efficient secure AI systems from, the core of your data center, all the way out to the edge. And so with NVIDIA we really have these converged accelerators where we've combined the GPU, which does all your AI processing with your data processing with the DPU. So we have this convergence really nice convergence of that area. And I would say the third area is really around our developer environment. One of the key, one of our key motivations at NVIDIA is really to have our partner ecosystem embrace our technology and build solutions around our technology. So if you look at what we've done with the DPU we've created an SDK, which is an open SDK called DOCA. And it's an open SDK for our partners to really build and develop solutions using BlueField and using all these accelerated libraries that we expose through DOCA. And so part of our differentiation is really building this open ecosystem for our partners to take advantage and build solutions around our technology. >> What's exciting is when I hear you talk it's like you realize that there's no one general purpose network anymore. Everyone has their own super environment, super cloud or these new capabilities. They can really craft their own I'd say custom environment at scale with easy tools. And it's all kind of that again this is the new architecture Mike, you were talking about. How does customers run this effectively, cost effectively? And how do people migrate? >> I think that is the key question. So we've got this beautiful architecture. Amazon Nitro is a good example of a SmartNIC architecture that has been successfully deployed but, enterprises and Tier 2 service providers and Tier 1 service providers and governments are not Amazon. So they need to migrate there and they need this architecture to be cost of effective. And that's super key. I mean, the reality is DPU are moving fast but they're not going to be deployed everywhere on day one. Some servers will have have DPUs right away. Some servers will have DPUs in a year or two. And then there are devices that may never have DPUs. IOT gateways, or legacy servers, even mainframes. So that's the beauty of a solution that creates a fabric across both the switch and the DPU. And by leveraging the NVIDIA BlueField DPU what we really like about it is, it's open and that drives cost efficiencies. And then, with this our architectural approach effectively you get a unified solution across switch and DPU, workload independent. It doesn't matter what hypervisor it is. Integrated visibility, integrated security and that can create tremendous cost efficiencies and really extract a lot of the expense from a capital perspective out of the network as well as from an operational perspective because now I have an SDN automated solution where I'm literally issuing a command to deploy a network service, or to deploy a security policy and is deployed everywhere automatically saving the network operations team and the security operations team time. >> So let me rewind that 'cause that's super important. Got the unified cloud architecture. I'm the customer, it's implemented. What's the value again, take me through the value to me. I have a unified environment. What's the value? >> I mean the value is effectively, there's a few pieces of value. The first piece of value is I'm creating this clean demark. I'm taking networking to the host. And like I mentioned, we're not running it on the CPU. So in implementations that run networking on the CPU there's some conflict between the DevOps team who own the server, and the NetOps team who own the network because they're installing software on the CPU stealing cycles from what should be revenue generating CPUs. So now by terminating the networking on the DPU we create this real clean demark. So the DevOps folks are happy because they don't necessarily have the skills to manage network and they don't necessarily want to spend the time managing networking. They've got their network counterparts who are also happy the NetOps team because they want to control the networking. And now we've got this clean demark where the DevOps folks get the services they need and the NetOps folks get the control and agility they need. So that's a huge value. The next piece of value is distributed security. This is essential I mentioned it earlier, pushing out micro segmentation and distributed firewall basically at the application level, where I create these small segments on an application by application basis. So if a bad actor does penetrate the perimeter firewall they're contained once they get inside. 'Cause the worst thing is a bad actor penetrates perimeter firewall and can go wherever they want in wreak havoc. And so that's why this is so essential. And the next benefit obviously is this unified networking operating model. Having an operating model across switch and server, underlay and overlay, workload agnostic, making the life of the NetOps teams much easier so they can focus their time on really strategy instead of spending an afternoon deploying a single VLAN for example. >> Awesome, and I think also for my stand point I mean perimeter security is pretty much, that out there, I guess the firewall still out there exists but pretty much they're being breached all the time the perimeter. You have to have this new security model. And I think the other thing that you mentioned the separation between DevOps is cool because the infrastructure is code is about making the developers be agile and build security in from day one. So this policy aspect is huge new control plan. I think you guys have a new architecture that enables the security to be handled more flexible. That seems to be the killer feature here. >> If you look at the data processing unit, I think one of the great things about sort of this new architecture it's really the foundation for zero trust. So like you talked about the perimeter is getting breached. And so now each and every compute node has to be protected. And I think that's sort of what you see with the partnership between Pluribus and NVIDIA is the DPU is really the foundation of zero trust and Pluribus is really building on that vision with allowing sort of micro-segmentation and being able to protect each and every compute node as well as the underlying network. >> This is super exciting. This is illustration of how the market's evolving architectures are being reshaped and refactored for cloud scale and all this new goodness with data. So I got to ask how you guys go into market together. Michael, start with you. What's the relationship look like in the go to market with NVIDIA? >> We're super excited about the partnership. Obviously we're here together. We think we've got a really good solution for the market so we're jointly marketing it. Obviously we appreciate that NVIDIA's open that's sort of in our DNA, we're about a open networking. They've got other ISVs who are going to run on BlueField-2. We're probably going to run on other DPUs in the future. But right now we feel like we're partnered with the number one provider of DPUs in the world and super excited about making a splash with it. >> Oh man NVIDIA got the hot product. >> So BlueField-2 as I mentioned was GA last year, we're introducing, well we now also have the converged accelerator. So I talked about artificial intelligence our artificial intelligence software with the BlueField DPU, all of that put together on a converged accelerator. The nice thing there is you can either run those workloads, so if you have an artificial intelligence workload and an infrastructure workload, you can work on them separately on the same platform or you can actually use you can actually run artificial intelligence applications on the BlueField itself. So that's what the converged accelerator really brings to the table. So that's available now. Then we have BlueField-3 which will be available late this year. And I talked about sort of, how much better that next generation of BlueField is in comparison to BlueField-2. So we'll see BlueField-3 shipping later on this year. And then our software stack which I talked about, which is called DOCA. We're on our second version, our DOCA 1.2 we're releasing DOCA 1.3 in about two months from now. And so that's really our open ecosystem framework. So allow you to program the BlueField. So we have all of our acceleration libraries, security libraries, that's all packed into this SDK called DOCA. And it really gives that simplicity to our partners to be able to develop on top of BlueField. So as we add new generations of BlueField, next year we'll have another version and so on and so forth. DOCA is really that unified layer that allows BlueField to be both forwards compatible and backwards compatible. So partners only really have to think about writing to that SDK once. And then it automatically works with future generations of BlueField. So that's sort of the nice thing around DOCA. And then in terms of our go to market model we're working with every major OEM. Later on this year you'll see, major server manufacturers releasing BlueField enabled servers, so more to come. >> Awesome, save money, make it easier, more capabilities, more workload power. This is the future of cloud operations. >> And one thing I'll add is we are, we have a number of customers as you'll hear in the next segment that are already signed up and will be working with us for our early field trial starting late April early May. We are accepting registrations. You can go to www.pluribusnetworks.com/eft. If you're interested in signing up for being part of our field trial and providing feedback on the product >> Awesome innovation and networking. Thanks so much for sharing the news. Really appreciate, thanks so much. In a moment we'll be back to look deeper in the product the integration, security, zero trust use cases. You're watching theCUBE, the leader in enterprise tech coverage. (upbeat music)

(upbeat music) >> Okay, we're back. I'm John Furrier with theCUBE and we're going to go deeper into a deep dive into unified cloud networking solution from Pluribus and NVIDIA. And we'll examine some of the use cases with Alessandro Barbieri, VP of product management at Pluribus Networks and Pete Lumbis, the director of technical marketing and video remotely. Guys thanks for coming on, appreciate it. >> Yeah thanks a lot. >> I'm happy to be here. >> So a deep dive, let's get into the what and how. Alessandro, we heard earlier about the Pluribus and NVIDIA partnership and the solution you're working together in. What is it? >> Yeah, first let's talk about the what. What are we really integrating with the NVIDIA BlueField the DPU technology? Pluribus has been shipping in volume in multiple mission critical networks, this Netvisor ONE network operating systems. It runs today on merchant silicon switches and effectively it's standard based open network operating system for data center. And the novelty about this operating system is that it integrates distributed the control plane to automate effect with SDN overlay. This automation is completely open and interoperable and extensible to other type of clouds. It's not enclosed. And this is actually what we're now porting to the NVIDIA DPU. >> Awesome, so how does it integrate into NVIDIA hardware and specifically how is Pluribus integrating its software with the NVIDIA hardware? >> Yeah, I think we leverage some of the interesting properties of the BlueField DPU hardware which allows actually to integrate our network operating system in a manner which is completely isolated and independent from the guest operating system. So the first byproduct of this approach is that whatever we do at the network level on the DPU card is completely agnostic to the hypervisor layer or OS layer running on the host. Even more, we can also independently manage this network node this switch on a NIC effectively, managed completely independently from the host. You don't have to go through the network operating system running on X86 to control this network node. So you truly have the experience effectively top of rack for virtual machine or a top of rack for Kubernetes spots, where if you allow me with analogy, instead of connecting a server NIC directly to a switchboard, now we are connecting a VM virtual interface to a virtual interface on the switch on an niche. And also as part of this integration, we put a lot of effort, a lot of emphasis in accelerating the entire data plan for networking and security. So we are taking advantage of the NVIDIA DOCA API to program the accelerators. And these you accomplish two things with that. Number one, you have much better performance. They're running the same network services on an X86 CPU. And second, this gives you the ability to free up I would say around 20, 25% of the server capacity to be devoted either to additional workloads to run your cloud applications or perhaps you can actually shrink the power footprint and compute footprint of your data center by 20% if you want to run the same number of compute workloads. So great efficiencies in the overall approach. >> And this is completely independent of the server CPU, right? >> Absolutely, there is zero code from Pluribus running on the X86. And this is why we think this enables a very clean demarcation between compute and network. >> So Pete, I got to get you in here. We heard that the DPU enable cleaner separation of DevOps and NetOps. Can you explain why that's important because everyone's talking DevSecOps, right? Now, you've got NetSecOps. This separation, why is this clean separation important? >> Yeah, I think, it's a pragmatic solution in my opinion. We wish the world was all kind of rainbows and unicorns, but it's a little messier than that. I think a lot of the DevOps stuff and that mentality and philosophy. There's a natural fit there. You have applications running on servers. So you're talking about developers with those applications integrating with the operators of those servers. Well, the network has always been this other thing and the network operators have always had a very different approach to things than compute operators. And I think that we in the networking industry have gotten closer together but there's still a gap, there's still some distance. And I think that distance isn't going to be closed and so, again, it comes down to pragmatism. And I think one of my favorite phrases is look, good fences make good neighbors. And that's what this is. >> Yeah, and it's a great point 'cause DevOps has become kind of the calling car for cloud, right? But DevOps is a simply infrastructures code and infrastructure is networking, right? So if infrastructure is code you're talking about that part of the stack under the covers, under the hood if you will. This is super important distinction and this is where the innovation is. Can you elaborate on how you see that because this is really where the action is right now? >> Yeah, exactly. And I think that's where one from the policy, the security, the zero trust aspect of this, right? If you get it wrong on that network side, all of a sudden you can totally open up those capabilities. And so security's part of that. But the other part is thinking about this at scale, right? So we're taking one top of rack switch and adding up to 48 servers per rack. And so that ability to automate, orchestrate and manage its scale becomes absolutely critical. >> Alessandro, this is really the why we're talking about here and this is scale. And again, getting it right. If you don't get it right, you're going to be really kind of up you know what? So this is a huge deal. Networking matters, security matters, automation matters, DevOps, NetOps, all coming together clean separation. Help us understand how this joint solution with NVIDIA fits into the Pluribus unified cloud networking vision because this is what people are talking about and working on right now. >> Yeah, absolutely. So I think here with this solution we're attacking two major problems in cloud networking. One, is operation of cloud networking and the second, is distributing security services in the cloud infrastructure. First, let me talk about first what are we really unifying? If we're unifying something, something must be at least fragmented or disjointed. And what is disjointed is actually the network in the cloud. If you look wholistically how networking is deployed in the cloud, you have your physical fabric infrastructure, right? Your switches and routers. You build your IP clause, fabric leaf and spine topologies. This is actually a well understood problem I would say. There are multiple vendors with let's say similar technologies, very well standardized, very well understood and almost a commodity I would say building an IP fabric these days, but this is not the place where you deploy most of your services in the cloud particularly from a security standpoint. Those services are actually now moved into the compute layer where cloud builders have to instrument a separate network virtualization layer where they deploy segmentation and security closer to the workloads. And this is where the complication arise. This high value part of the cloud network is where you have a plethora of options that they don't talk to each other and they're very dependent on the kind of hypervisor or compute solution you choose. For example, the networking API between an ESXi environment or an Hyper-V or a Zen are completely disjointed. You have multiple orchestration layers. And then when you throw in also Kubernetes in this type of architecture, you are introducing yet another level of networking. And when Kubernetes runs on top of VMs which is a prevalent approach, you actually are stuck in multiple networks on the compute layer that they eventually ran on the physical fabric infrastructure. Those are all ships in the knights effectively, right? They operate as completely disjointed and we're trying to tackle this problem first with the notion of a unified fabric which is independent from any workloads whether this fabric spans on a switch which can be connected to bare metal workload or can span all the way inside the DPU where you have your multi hypervisor compute environment. It's one API, one common network control plane and one common set of segmentation services for the network. That's problem number one. >> It's interesting I hear you talking and I hear one network among different operating models. Reminds me of the old serverless days. There's still servers but they call it serverless. Is there going to be a term network-less because at the end of the day it should be one network, not multiple operating models. This is a problem that you guys are working on, is that right? I'm just joking serverless and network-less, but the idea is it should be one thing. >> Yeah, effectively what we're trying to do is we're trying to recompose this fragmentation in terms of network cooperation across physical networking and server networking. Server networking is where the majority of the problems are because as much as you have standardized the ways of building physical networks and cloud fabrics with IP protocols and internet, you don't have that sort of operational efficiency at the server layer. And this is what we're trying to attack first with this technology. The second aspect we're trying to attack is how we distribute security services throughout the infrastructure more efficiently whether it's micro-segmentation is a stateful firewall services or even encryption. Those are all capabilities enabled by the BlueField DPU technology. And we can actually integrate those capabilities directly into the network fabric limiting dramatically at least for east west traffic the sprawl of security appliances whether virtual or physical. That is typically the way people today segment and secure the traffic in the cloud. >> Awesome. Pete, all kidding aside about network-less and serverless kind of fun play on words there, the network is one thing it's basically distributed computing, right? So I'd love to get your thoughts about this distributed security with zero trust as the driver for this architecture you guys are doing. Can you share in more detail the depth of why DPU based approach is better than alternatives? >> Yeah, I think what's beautiful and kind of what the DPU brings that's new to this model is completely isolated compute environment inside. So it's the, yo dog, I heard you like a server so I put a server inside your server. And so we provide ARM CPUs, memory and network accelerators inside and that is completely isolated from the host. The actual X86 host just thinks it has a regular niche in there, but you actually have this full control plane thing. It's just like taking your top of rack switch and shoving it inside of your compute node. And so you have not only this separation within the data plane, but you have this complete control plane separation so you have this element that the network team can now control and manage, but we're taking all of the functions we used to do at the top of rack switch and we're distributing them now. And as time has gone on we've struggled to put more and more and more into that network edge. And the reality is the network edge is the compute layer, not the top of rack switch layer. And so that provides this phenomenal enforcement point for security and policy. And I think outside of today's solutions around virtual firewalls, the other option is centralized appliances. And even if you can get one that can scale large enough, the question is, can you afford it? And so what we end up doing is we kind of hope that NVIDIA's good enough or we hope that the VXLAN tunnel's good enough. And we can't actually apply more advanced techniques there because we can't financially afford that appliance to see all of the traffic. And now that we have a distributed model with this accelerator, we could do it. >> So what's in it for the customer real quick and I think this is an interesting point you mentioned policy. Everyone in networking knows policy is just a great thing. And as you hear it being talked about up the stack as well when you start getting to orchestrating microservices and whatnot all that good stuff going on there, containers and whatnot and modern applications. What's the benefit to the customers with this approach because what I heard was more scale, more edge, deployment flexibility relative to security policies and application enablement? What's the customer get out of this architecture? What's the enablement? >> It comes down to taking again the capabilities that we're in that top of rack switch and distributing them down. So that makes simplicity smaller, blast radius' for failures smaller failure domains, maintenance on the networks and the systems become easier. Your ability to integrate across workloads becomes infinitely easier. And again, we always want to kind of separate each one of those layers so just as in say a VXLAN network, my leaf in spine don't have to be tightly coupled together. I can now do this at a different layer and so you can run a DPU with any networking in the core there. And so you get this extreme flexibility. You can start small, you can scale large. To me the possibilities are endless. >> It's a great security control plan. Really flexibility is key and also being situationally aware of any kind of threats or new vectors or whatever's happening in the network. Alessandro, this is huge upside, right? You've already identified some successes with some customers on your early field trials. What are they doing and why are they attracted to the solution? >> Yeah, I think the response from customer has been the most encouraging and exciting for us to sort of continue and work and develop this product. And we have actually learned a lot in the process. We talked to tier two, tier three cloud providers. We talked to SP, Soft Telco type of networks as well as inter large enterprise customers. In one particular case one, let me call out a couple of examples here just to give you a flavor. There is a cloud provider in Asia who is actually managing a cloud where they're offering services based on multiple hypervisors. They are native services based on Zen, but they also on ramp into the cloud workloads based on ESXi and KVM depending on what the customer picks from the menu. And they have the problem of now orchestrating through their orchestrate or integrating with Zen center, with vSphere, with OpenStack to coordinate this multiple environments. And in the process to provide security, they actually deploy virtual appliances everywhere which has a lot of cost complication and eats up into the server CPU. The promise that they saw in this technology, they call it actually game changing is actually to remove all this complexity, having a single network and distribute the micro segmentation service directly into the fabric. And overall they're hoping to get out it tremendous OPEX benefit and overall operational simplification for the cloud infrastructure. That's one important use case. Another global enterprise customer is running both ESXi and Hyper-V environment and they don't have a solution to do micro segmentation consistently across hypervisors. So again, micro segmentation is a huge driver security. Looks like it's a recurring theme talking to most of these customers. And in the Telco space, we're working with few Telco customers on the CFT program where the main goal is actually to harmonize network cooperation. They typically handle all the VNFs with their own homegrown DPDK stack. This is overly complex. It is frankly also slow and inefficient. And then they have a physical network to manage. The idea of having again one network to coordinate the provisioning of cloud services between the Telco VNFs and the rest of the infrastructure is extremely powerful on top of the offloading capability opted by the BlueField DPUs. Those are just some examples. >> That was a great use case. A lot more potential I see that with the unified cloud networking, great stuff, Pete, shout out to you 'cause at NVIDIA we've been following your success us for a long time and continuing to innovate as cloud scales and Pluribus with unified networking kind of bring it to the next level. Great stuff, great to have you guys on and again, software keeps driving the innovation and again, networking is just a part of it and it's the key solution. So I got to ask both of you to wrap this up. How can cloud operators who are interested in this new architecture and solution learn more because this is an architectural shift? People are working on this problem, they're try to think about multiple clouds, they're try to think about unification around the network and giving more security, more flexibility to their teams. How can people learn more? >> Yeah, so Alessandro and I have a talk at the upcoming NVIDIA GTC conference. So it's the week of March 21st through 24th. You can go and register for free nvidia.com/gtc. You can also watch recorded sessions if you end up watching this on YouTube a little bit after the fact. And we're going to dive a little bit more into the specifics and the details and what we're providing in the solution. >> Alessandro, how can we people learn more? >> Yeah, absolutely. People can go to the Pluribus website, www.pluribusnetworks.com/eft and they can fill up the form and they will contact Pluribus to either know more or to know more and actually to sign up for the actual early field trial program which starts at the end of April. >> Okay, well, we'll leave it there. Thank you both for joining, appreciate it. Up next you're going to hear an independent analyst perspective and review some of the research from the enterprise strategy group ESG. I'm John Furrier with theCUBE, thanks for watching. (upbeat music)

>>Well, welcome back to the cubes coverage of AWS reinvent 2021. We're here joined by Ian buck, general manager and vice president of accelerated computing at Nvidia I'm. John Ford, your host of the QB. And thanks for coming on. So in video, obviously, great brand congratulates on all your continued success. Everyone who has does anything in graphics knows the GPU's are hot and you guys get great brand great success in the company, but AI and machine learning was seeing the trend significantly being powered by the GPU's and other systems. So it's a key part of everything. So what's the trends that you're seeing, uh, in ML and AI, that's accelerating computing to the cloud. Yeah, >>I mean, AI is kind of drape bragging breakthroughs innovations across so many segments, so many different use cases. We see it showing up with things like credit card, fraud prevention and product and content recommendations. Really it's the new engine behind search engines is AI. Uh, people are applying AI to things like, um, meeting transcriptions, uh, virtual calls like this using AI to actually capture what was said. Um, and that gets applied in person to person interactions. We also see it in intelligence systems assistance for a contact center, automation or chat bots, uh, medical imaging, um, and intelligence stores and warehouses and everywhere. It's really, it's really amazing what AI has been demonstrated, what it can do. And, uh, it's new use cases are showing up all the time. >>Yeah. I'd love to get your thoughts on, on how the world's evolved just in the past few years, along with cloud, and certainly the pandemics proven it. You had this whole kind of full stack mindset initially, and now you're seeing more of a horizontal scale, but yet enabling this vertical specialization in applications. I mean, you mentioned some of those apps, the new enablers, this kind of the horizontal play with enablement for specialization, with data, this is a huge shift that's going on. It's been happening. What's your reaction to that? >>Yeah, it's the innovations on two fronts. There's a horizontal front, which is basically the different kinds of neural networks or AIS as well as machine learning techniques that are, um, just being invented by researchers for, uh, and the community at large, including Amazon. Um, you know, it started with these convolutional neural networks, which are great for image processing, but as it expanded more recently into, uh, recurrent neural networks, transformer models, which are great for language and language and understanding, and then the new hot topic graph neural networks, where the actual graph now is trained as a, as a neural network, you have this underpinning of great AI technologies that are being adventure around the world in videos role is try to productize that and provide a platform for people to do that innovation and then take the next step and innovate vertically. Um, take it, take it and apply it to two particular field, um, like medical, like healthcare and medical imaging applying AI, so that radiologists can have an AI assistant with them and highlight different parts of the scan. >>Then maybe troublesome worrying, or requires more investigation, um, using it for robotics, building virtual worlds, where robots can be trained in a virtual environment, their AI being constantly trained, reinforced, and learn how to do certain activities and techniques. So that the first time it's ever downloaded into a real robot, it works right out of the box, um, to do, to activate that we co we are creating different vertical solutions, vertical stacks for products that talk the languages of those businesses, of those users, uh, in medical imaging, it's processing medical data, which is obviously a very complicated large format data, often three-dimensional boxes in robotics. It's building combining both our graphics and simulation technologies, along with the, you know, the AI training capabilities and different capabilities in order to run in real time. Those are, >>Yeah. I mean, it's just so cutting edge. It's so relevant. I mean, I think one of the things you mentioned about the neural networks, specifically, the graph neural networks, I mean, we saw, I mean, just to go back to the late two thousands, you know, how unstructured data or object store created, a lot of people realize that the value out of that now you've got graph graph value, you got graph network effect, you've got all kinds of new patterns. You guys have this notion of graph neural networks. Um, that's, that's, that's out there. What is, what is a graph neural network and what does it actually mean for deep learning and an AI perspective? >>Yeah, we have a graph is exactly what it sounds like. You have points that are connected to each other, that established relationships and the example of amazon.com. You might have buyers, distributors, sellers, um, and all of them are buying or recommending or selling different products. And they're represented in a graph if I buy something from you and from you, I'm connected to those end points and likewise more deeply across a supply chain or warehouse or other buyers and sellers across the network. What's new right now is that those connections now can be treated and trained like a neural network, understanding the relationship. How strong is that connection between that buyer and seller or that distributor and supplier, and then build up a network that figure out and understand patterns across them. For example, what products I may like. Cause I have this connection in my graph, what other products may meet those requirements, or also identifying things like fraud when, when patterns and buying patterns don't match, what a graph neural networks should say would be the typical kind of graph connectivity, the different kind of weights and connections between the two captured by the frequency half I buy things or how I rate them or give them stars as she used cases, uh, this application graph neural networks, which is basically capturing the connections of all things with all people, especially in the world of e-commerce, it's very exciting to a new application, but applying AI to optimizing business, to reducing fraud and letting us, you know, get access to the products that we want, the products that they have, our recommendations be things that, that excited us and want us to buy things >>Great setup for the real conversation that's going on here at re-invent, which is new kinds of workloads are changing. The game. People are refactoring their business with not just replatform, but actually using this to identify value and see cloud scale allows you to have the compute power to, you know, look at a note on an arc and actually code that. It's all, it's all science, all computer science, all at scale. So with that, that brings up the whole AWS relationship. Can you tell us how you're working with AWS before? >>Yeah. 80 of us has been a great partner and one of the first cloud providers to ever provide GPS the cloud, uh, we most more recently we've announced two new instances, uh, the instance, which is based on the RA 10 G GPU, which has it was supports the Nvidia RTX technology or rendering technology, uh, for real-time Ray tracing and graphics and game streaming is their highest performance graphics, enhanced replicate without allows for those high performance graphics applications to be directly hosted in the cloud. And of course runs everything else as well, including our AI has access to our AI technology runs all of our AI stacks. We also announced with AWS, the G 5g instance, this is exciting because it's the first, uh, graviton or ARM-based processor connected to a GPU and successful in the cloud. Um, this makes, uh, the focus here is Android gaming and machine learning and France. And we're excited to see the advancements that Amazon is making and AWS is making with arm and the cloud. And we're glad to be part of that journey. >>Well, congratulations. I remember I was just watching my interview with James Hamilton from AWS 2013 and 2014. He was getting, he was teasing this out, that they're going to build their own, get in there and build their own connections, take that latency down and do other things. This is kind of the harvest of all that. As you start looking at these new new interfaces and the new servers, new technology that you guys are doing, you're enabling applications. What does, what do you see this enabling as this, as this new capability comes out, new speed, more, more performance, but also now it's enabling more capabilities so that new workloads can be realized. What would you say to folks who want to ask that question? >>Well, so first off I think arm is here to stay and you can see the growth and explosion of my arm, uh, led of course, by grab a tiny to be. I spend many others, uh, and by bringing all of NVIDIA's rendering graphics, machine learning and AI technologies to arm, we can help bring that innovation. That arm allows that open innovation because there's an open architecture to the entire ecosystem. Uh, we can help bring it forward, uh, to the state of the art in AI machine learning, the graphics. Um, we all have our software that we released is both supportive, both on x86 and an army equally, um, and including all of our AI stacks. So most notably for inference the deployment of AI models. We have our, the Nvidia Triton inference server. Uh, this is the, our inference serving software where after he was trained to model, he wanted to play it at scale on any CPU or GPU instance, um, for that matter. So we support both CPS and GPS with Triton. Um, it's natively integrated with SageMaker and provides the benefit of all those performance optimizations all the time. Uh, things like, uh, features like dynamic batching. It supports all the different AI frameworks from PI torch to TensorFlow, even a generalized Python code. Um, we're activating how activating the arm ecosystem as well as bringing all those AI new AI use cases and all those different performance levels, uh, with our partnership with AWS and all the different clouds. >>And you got to making it really easy for people to use, use the technology that brings up the next kind of question I want to ask you. I mean, a lot of people are really going in jumping in the big time into this. They're adopting AI. Either they're moving in from prototype to production. There's always some gaps, whether it's knowledge, skills, gaps, or whatever, but people are accelerating into the AI and leaning into it hard. What advancements have is Nvidia made to make it more accessible, um, for people to move faster through the, through the system, through the process? >>Yeah, it's one of the biggest challenges. The other promise of AI, all the publications that are coming all the way research now, how can you make it more accessible or easier to use by more people rather than just being an AI researcher, which is, uh, uh, obviously a very challenging and interesting field, but not one that's directly in the business. Nvidia is trying to write a full stack approach to AI. So as we make, uh, discover or see these AI technologies come available, we produce SDKs to help activate them or connect them with developers around the world. Uh, we have over 150 different STKs at this point, certain industries from gaming to design, to life sciences, to earth scientist. We even have stuff to help simulate quantum computing. Um, and of course all the, all the work we're doing with AI, 5g and robotics. So, uh, we actually just introduced about 65 new updates just this past month on all those SDKs. Uh, some of the newer stuff that's really exciting is the large language models. Uh, people are building some amazing AI. That's capable of understanding the Corpus of like human understanding, these language models that are trained on literally the continent of the internet to provide general purpose or open domain chatbots. So the customer is going to have a new kind of experience with a computer or the cloud. Uh, we're offering large language, uh, those large language models, as well as AI frameworks to help companies take advantage of this new kind of technology. >>You know, each and every time I do an interview with Nvidia or talk about Nvidia my kids and their friends, they first thing they said, you get me a good graphics card. Hey, I want the best thing in their rig. Obviously the gaming market's hot and known for that, but I mean, but there's a huge software team behind Nvidia. This is a well-known your CEO is always talking about on his keynotes, you're in the software business. And then you had, do have hardware. You were integrating with graviton and other things. So, but it's a software practices, software. This is all about software. Could you share kind of more about how Nvidia culture and their cloud culture and specifically around the scale? I mean, you, you hit every, every use case. So what's the software culture there at Nvidia, >>And it is actually a bigger, we have more software people than hardware people, people don't often realize this. Uh, and in fact that it's because of we create, uh, the, the, it just starts with the chip, obviously building great Silicon is necessary to provide that level of innovation, but as it expanded dramatically from then, from there, uh, not just the Silicon and the GPU, but the server designs themselves, we actually do entire server designs ourselves to help build out this infrastructure. We consume it and use it ourselves and build our own supercomputers to use AI, to improve our products. And then all that software that we build on top, we make it available. As I mentioned before, uh, as containers on our, uh, NGC container store container registry, which is accessible for me to bus, um, to connect to those vertical markets, instead of just opening up the hardware and none of the ecosystem in develop on it, they can with a low-level and programmatic stacks that we provide with Kuda. We believe that those vertical stacks are the ways we can help accelerate and advance AI. And that's why we make as well, >>Ram a little software is so much easier. I want to get that plug for, I think it's worth noting that you guys are, are heavy hardcore, especially on the AI side. And it's worth calling out, uh, getting back to the customers who are bridging that gap and getting out there, what are the metrics they should consider as they're deploying AI? What are success metrics? What does success look like? Can you share any insight into what they should be thinking about and looking at how they're doing? >>Yeah. Um, for training, it's all about time to solution. Um, it's not the hardware that that's the cost, it's the opportunity that AI can provide your business and many, and the productivity of those data scientists, which are developing, which are not easy to come by. So, uh, what we hear from customers is they need a fast time to solution to allow people to prototype very quickly, to train a model to convergence, to get into production quickly, and of course, move on to the next or continue to refine it often. So in training is time to solution for inference. It's about our, your ability to deploy at scale. Often people need to have real time requirements. They want to run in a certain amount of latency, a certain amount of time. And typically most companies don't have a single AI model. They have a collection of them. They want, they want to run for a single service or across multiple services. That's where you can aggregate some of your infrastructure leveraging the trading infant server. I mentioned before can actually run multiple models on a single GPU saving costs, optimizing for efficiency yet still meeting the requirements for latency and the real time experience so that your customers have a good, a good interaction with the AI. >>Awesome. Great. Let's get into, uh, the customer examples. You guys have obviously great customers. Can you share some of the use cases, examples with customers, notable customers? >>Yeah. I want one great part about working in videos as a technology company. You see, you get to engage with such amazing customers across many verticals. Uh, some of the ones that are pretty exciting right now, Netflix is using the G4 instances to CLA um, to do a video effects and animation content. And, you know, from anywhere in the world, in the cloud, uh, as a cloud creation content platform, uh, we work in the energy field that Siemens energy is actually using AI combined with, um, uh, simulation to do predictive maintenance on their energy plants, um, and, and, uh, doing preventing or optimizing onsite inspection activities and eliminating downtime, which is saving a lot of money for the engine industry. Uh, we have worked with Oxford university, uh, which is Oxford university actually has over two, over 20 million artifacts and specimens and collections across its gardens and museums and libraries. They're actually using convenient GPS and Amazon to do enhance image recognition, to classify all these things, which would take literally years with, um, uh, going through manually each of these artifacts using AI, we can click and quickly catalog all of them and connect them with their users. Um, great stories across graphics, about cross industries across research that, uh, it's just so exciting to see what people are doing with our technology together with, >>And thank you so much for coming on the cube. I really appreciate Greg, a lot of great content there. We probably going to go another hour, all the great stuff going on in the video, any closing remarks you want to share as we wrap this last minute up >>Now, the, um, really what Nvidia is about as accelerating cloud computing, whether it be AI, machine learning, graphics, or headphones, community simulation, and AWS was one of the first with this in the beginning, and they continue to bring out great instances to help connect, uh, the cloud and accelerated computing with all the different opportunities integrations with with SageMaker really Ks and ECS. Uh, the new instances with G five and G 5g, very excited to see all the work that we're doing together. >>Ian buck, general manager, and vice president of accelerated computing. I mean, how can you not love that title? We want more, more power, more faster, come on. More computing. No, one's going to complain with more computing know, thanks for coming on. Thank you. Appreciate it. I'm John Farrell hosted the cube. You're watching Amazon coverage reinvent 2021. Thanks for watching.

from the cube studios in palo alto in boston bringing you data-driven insights from the cube and etr this is breaking analysis with dave vellante nvidia wants to completely transform enterprise computing by making data centers run 10x faster at one tenth the cost and video's ceo jensen wang is crafting a strategy to re-architect today's on-prem data centers public clouds and edge computing installations with a vision that leverages the company's strong position in ai architectures the keys to this end-to-end strategy include a clarity of vision massive chip design skills a new arm-based architecture approach that integrates memory processors i o and networking and a compelling software consumption model even if nvidia is unsuccessful at acquiring arm we believe it will still be able to execute on this strategy by actively participating in the arm ecosystem however if its attempts to acquire arm are successful we believe it will transform nvidia from the world's most valuable chip company into the world's most valuable supplier of integrated computing architectures hello everyone and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll explain why we believe nvidia is in the right position to power the world's computing centers and how it plans to disrupt the grip that x86 architectures have had on the data center for decades the data center market is in transition like the universe the cloud is expanding at an accelerated pace no longer is the cloud an opaque set of remote services i always say somewhere out there sitting in a mega data center no rather the cloud is extending to on-premises data centers data centers are moving into the cloud and they're connecting through adjacent locations that create hybrid interactions clouds are being meshed together across regions and eventually will stretch to the far edge this new definition or view of cloud will be hyper distributed and run by software kubernetes is changing the world of software development and enabling workloads to run anywhere open apis external applications expanding the digital supply chains and this expanding cloud they all increase the threat surface and vulnerability to the most sensitive information that resides within the data center and around the world zero trust has become a mandate we're also seeing ai being injected into every application and it's the technology area that we see with the most momentum coming out of the pandemic this new world will not be powered by general purpose x86 processors rather it will be supported by an ecosystem of arm-based providers in our opinion that are affecting an unprecedented increase in processor performance as we have been reporting and nvidia in our view is sitting in the poll position and is currently the favorite to dominate the next era of computing architecture for global data centers public clouds as well as the near and far edge let's talk about jensen wang's clarity of vision for this new world here's a chart that underscores some of the fundamental assumptions that he's leveraging to expand his market the first is that there's a lot of waste in the data center he claims that only half of the cpu cores deployed in the data center today actually support applications the other half are processing the infrastructure all around the applications that run the software defined data center and they're terribly under utilized nvidia's blue field three dpu the data processing unit was described in a blog post on siliconangle by analyst zias caravala as a complete mini server on a card i like that with software defined networking storage and security acceleration built in this product has the bandwidth and according to nvidia can replace 300 general purpose x86 cores jensen believes that every network chip will be intelligent programmable and capable of this type of acceleration to offload conventional cpus he believes that every server node will have this capability and enable every packed of every packet and every application to be monitored in real time all the time for intrusion and as servers move to the edge bluefield will be included as a core component in his view and this last statement by jensen is critical in our opinion he says ai is the most powerful force of our time whether you agree with that or not it's relevant because ai is everywhere an invidious position in ai and the architectures the company is building are the fundamental linchpin of its data center enterprise strategy so let's take a look at some etr spending data to see where ai fits on the priority list here's a set of data in a view that we often like to share the horizontal axis is market share or pervasiveness in the etr data but we want to call your attention to the vertical axis that's really really what really we want to pay attention today that's net score or spending momentum exiting the pandemic we've seen ai capture the number one position in the last two surveys and we think this dynamic will continue for quite some time as ai becomes the staple of digital transformations and automations an ai will be infused in every single dot you see on this chart nvidia's architectures it just so happens are tailor made for ai workloads and that is how it will enter these markets let's quantify what that means and lay out our view of how nvidia with the help of arm will go after the enterprise market here's some data from wikibon research that depicts the percent of worldwide spending on server infrastructure by workload type here are the key points first the market last year was around 78 billion dollars worldwide and is expected to approach 115 billion by the end of the decade this might even be a conservative figure and we've split the market into three broad workload categories the blue is ai and other related applications what david floyer calls matrix workloads the orange is general purpose think things like erp supply chain hcm collaboration basically oracle saps and microsoft work that's being supported today and of course many other software providers and the gray that's the area that jensen was referring to is about being wasted the offload work for networking and storage and all the software defined management in the data centers around the world okay you can see the squeeze that we think compute infrastructure is gonna gonna occur around that orange area that general-purpose workloads that we think is going to really get squeezed in the next several years on a percentage basis and on an absolute basis it's really not growing nearly as fast as the other two and video with arm in our view is well positioned to attack that blue area and the gray area those those workload offsets and the new emerging ai applications but even the orange as we've reported is under pressure as for example companies like aws and oracle they use arm-based designs to service general purpose workloads why are they doing that cost is the reason because x86 generally and intel specifically are not delivering the price performance and efficiency required to keep up with the demands to reduce data center costs and if intel doesn't respond which we believe it will but if it doesn't act arm we think will get 50 percent of the general purpose workloads by the end of the decade and with nvidia it will dominate the blue the ai and the gray the offload work when we say dominate we're talking like capture 90 percent of the available market if intel doesn't respond now intel they're not just going to sit back and let that happen pat gelsinger is well aware of this in moving intel to a new strategy but nvidia and arm are way ahead in the game in our view and as we've reported this is going to be a real challenge for intel to catch up now let's take a quick look at what nvidia is doing with relevant parts of its pretty massive portfolio here's a slide that shows nvidia's three chip strategy the company is shifting to arm-based architectures which we'll describe in more detail in a moment the slide shows at the top line nvidia's ampere architecture not to be confused with the company ampere computing nvidia is taking a gpu centric approach no surprise obvious reasons there that's their sort of stronghold but we think over time it may rethink this a little bit and lean more into npus the neural processing unit we look at what apple's doing what tesla are doing we see opportunities for companies like nvidia to really sort of go after that but we'll save that for another day nvidia has announced its grace cpu a nod to the famous computer scientist grace hopper grace is a new architecture that doesn't rely on x86 and much more efficiently uses memory resources we'll again describe this in more detail later and the bottom line there that roadmap line shows the bluefield dpu which we described is essentially a complete server on a card in this approach using arm will reduce the elapsed time to go from chip design to production by 50 we're talking about shaving years down to 18 months or less we don't have time to do a deep dive into nvidia's portfolio it's large but we want to share some things that we think are important and this next graphic is one of them this shows some of the details of nvidia's jetson architecture which is designed to accelerate those ai plus workloads that we showed earlier and the reason is that this is important in our view is because the same software supports from small to very large including edge systems and we think this type of architecture is very well suited for ai inference at the edge as well as core data center applications that use ai and as we've said before a lot of the action in ai is going to happen at the edge so this is a good example of leveraging an architecture across a wide spectrum of performance and cost now we want to take a moment to explain why the moved arm-based architectures is so critical to nvidia one of the biggest cost challenges for nvidia today is keeping the gpu utilized typical utilization of gpu is well below 20 percent here's why the left hand side of this chart shows essentially racks if you will of traditional compute and the bottlenecks that nvidia faces the processor and dram they're tied together in separate blocks imagine there are thousands thousands of cores in a rack and every time you need data that lives in another processor you have to send a request and go retrieve it it's very overhead intensive now technologies like rocky are designed to help but it doesn't solve the fundamental architectural bottleneck every gpu shown here also has its own dram and it has to communicate with the processors to get the data i.e they can't communicate with each other efficiently now the right hand side side shows where nvidia is headed start in the middle with system on chip socs cpus are packaged in with npus ipu's that's the image processing unit you know x dot dot dot x pu's the the alternative processors they're all connected with sram which is think of that as a high speed layer like an layer one cache the os for the system on a chip lives inside of this and that's where nvidia has this killer software model what they're doing is they're licensing the consumption of the operating system that's running this system on chip in this entire system and they're affecting a new and really compelling subscription model you know maybe they should just give away the chips and charge for the software like a razer blade model talk about disruptive now the outer layer is the the dpu and the shared dram and other resources like the ampere computing the company this time cpus ssds and other resources these are the processors that will manage the socs together this design is based on nvidia's three chip approach using bluefield dpu leveraging melanox that's the networking component the network enables shared dram across the cpus which will eventually be all arm based grace lives inside the system on a chip and also on the outside layers and of course the gpu lives inside the soc in a scaled-down version like for instance a rendering gpu and we show some gpus on the outer layer as well for ai workloads at least in the near term you know eventually we think they may reside solely in the system on chip but only time will tell okay so you as you can see nvidia is making some serious moves and by teaming up with arm and leaning into the arm ecosystem it plans to take the company to its next level so let's talk about how we think competition for the next era of compute stacks up here's that same xy graph that we love to show market share or pervasiveness on the horizontal tracking against next net score on the vertical net score again is spending velocity and we've cut the etr data to capture players that are that are big in compute and storage and networking we've plugged in a couple of the cloud players these are the guys that we feel are vying for data center leadership around compute aws is a very strong position we believe that more than half of its revenues comes from compute you know ec2 we're talking about more than 25 billion on a run rate basis that's huge the company designs its own silicon graviton 2 etc and is working with isvs to run general purpose workloads on arm-based graviton chips microsoft and google they're going to follow suit they're big consumers of compute they sell a lot but microsoft in particular you know they're likely to continue to work with oem partners to attack that on-prem data center opportunity but it's really intel that's the provider of compute to the likes of hpe and dell and cisco and the odms which are the odms are not shown here now hpe let's talk about them for a second they have architectures and i hate to bring it up but remember the machine i know it's the butt of many jokes especially from competitors it had been you know frankly hpe and hp they deserve some of that heat for all the fanfare and then that they they put out there and then quietly you know pulled the machine or put it out the pasture but hpe has a strong position in high performance computing and the work that it did on new computing architectures with the machine and shared memories that might be still kicking around somewhere inside of hp and could come in handy for some day in the future so hpe has some chops there plus hpe has been known hp historically has been known to design its own custom silicon so i would not count them out as an innovator in this race cisco is interesting because it not only has custom silicon designs but its entry into the compute business with ucs a decade ago was notable and they created a new way to think about integrating resources particularly compute and networking with partnerships to add in the storage piece initially it was within within emc prior to the dell acquisition but you know it continues with netapp and pure and others cisco invests they spend money investing in architectures and we expect the next generation of ucs oh ucs2 ucs 2.0 will mark another notable milestone in the company's data center business dell just had an amazing quarterly earnings report the company grew top line revenue by around 12 percent and it wasn't because of an easy compare to last year dells is simply executing despite continued softness in the legacy emc storage business laptop the laptop demand continued to soar in dell server business it's growing again but we don't see dell as an architectural innovator per se in compute rather we think the company will be content to partner with suppliers whether it's intel nvidia arm-based partners or all of the above dell we think will rely on its massive portfolio its excellent supply chain and execution ethos to compete now ibm is notable for historical reasons with its mainframe ibm created the first great compute monopoly before it unwind and wittingly handed it to intel along with microsoft we don't see ibm necessarily aspiring to retake that compute platform mantle that once once held with mainframes rather red hat in the march to hybrid cloud is the path that we think in our view is ibm's approach now let's get down to the elephants in the room intel nvidia and china inc china is of course relevant because of companies like alibaba and huawei and the chinese chinese government's desire to be self-sufficient in semiconductor technology and technology generally but our premise here is that the trends are favoring nvidia over intel in this picture because nvidia is making moves to further position itself for new workloads in the data center and compete for intel's stronghold intel is going to attempt to remake itself but it should have been doing this seven years ago what pat gelsinger is doing today intel is simply far behind and it's going to take at least a couple years for them to really start to to make inroads in this new model let's stay on the nvidia v intel comparison for a moment and take a snapshot of the two companies here's a quick chart that we put together with some basic kpis some of these figures are approximations or they're rounded so don't stress over it too much but you can see intel is an 80 billion dollar company 4x the size of nvidia but nvidia's market cap far exceeds that of intel why is that of course growth in our view it's justified due to that growth and nvidia's strategic positioning intel used to be the gross margin king but nvidia has much higher gross margins interesting now when it comes down to free cash flow intel is still dominant as it pertains to the balance sheet intel is way more capital intensive than nvidia and as it starts to build out its foundries that's going to eat into intel's cash position now what we did is we put together a little pro forma on the third column of nvidia plus arm circa let's say the end of 2022. we think they could get to a run rate that is about half the size of intel and that can propel the company's market cap to well over half a trillion dollars if they get any credit for arm they're paying 40 billion dollars for arm a company that's you know sub 2 billion the risk is that because of the arm because the arm deal is based on cash plus tons of stock it could put pressure on the market capitalization for some time arm has 90 percent gross margins because it pretty much has a pure license model so it helps the gross margin line a little bit for this in this pro forma and the balance sheet is a swag arm has said that it's not going to take on debt to do the transaction but we haven't had time to really dig into that and figure out how they're going to structure it so we took a took a swag in in what we would do with this low interest rate environment but but take that with a grain of salt we'll do more research in there the point is given the momentum and growth of nvidia its strategic position in ai is in its deep engineering they're aimed at all the right places and its potential to unlock huge value with arm on paper it looks like the horse to beat if it can execute all right let's wrap up here's a summary look the architectures on which nvidia is building its dominant ai business are evolving and nvidia is well positioned to drive a truck right to the enterprise in our view the power has shifted from intel to the arm ecosystem and nvidia is leaning in big time whereas intel it has to preserve its current business while recreating itself at the same time this is going to take a couple of years but intel potentially has the powerful backing of the us government too strategic to fail the wild card is will nvidia be successful in acquiring arm certain factions in the uk and eu are fighting the deal because they don't want the u.s dictating to whom arm can sell its technology for example the restrictions placed on huawei for many suppliers of arm-based chips based on u.s sanctions nvidia's competitors like broadcom qualcomm at all are nervous that if nvidia gets armed they will be at a competitive disadvantage they being invidious competitors and for sure china doesn't want nvidia controlling arm for obvious reasons and it will do what it can to block the deal and or put handcuffs on how business can be done in china we can see a scenario where the u.s government pressures the uk and eu regulators to let this deal go through look ai and semiconductors you can't get much more strategic than that for the u.s military and the u.s long-term competitiveness in exchange for maybe facilitating the deal the government pressures nvidia to guarantee some feed to the intel foundry business while at the same time imposing conditions that secure access to arm-based technology for nvidia's competitors and maybe as we've talked about before having them funnel business to intel's foundry actually we've talked about the us government enticing apple to do so but it could also entice nvidia's competitors to do so propping up intel's foundry business which is clearly starting from ground zero and is going to need help outside of intel's own semiconductor manufacturing internally look we don't have any inside information as to what's happening behind the scenes with the us government and so forth but on its earning call on its earnings call nvidia said they're working with regulators that are on track to complete the deal in early 2022. we'll see okay that's it for today thank you to david floyer who co-created this episode with me and remember i publish each week on wikibon.com and siliconangle.com these episodes they're all available as podcasts all you're going to do is search breaking analysis podcast and you can always connect with me on twitter at dvalante or email me at david.valante siliconangle.com i always appreciate the comments on linkedin and in the clubhouse please follow me so you can be notified when we start a room and riff on these topics and don't forget to check out etr.plus for all the survey data this is dave vellante for the cube insights powered by etr be well and we'll see you next time [Music] you

>> Narrator: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hi, I'm Stu Miniman, and welcome to a CUBE conversation. I'm coming to you from our Boston Area studio. And we're going to be digging into some interesting news regarding networking. Some important use cases these days, in 2020, of course, AI is a big piece of it. So happy to welcome to the program. First of all, I have one of our CUBE alumni, Kevin Deierling. He's the Senior Vice President of Marketing with Nvidia, part of the networking team there. And joining him is Scott Tease, someone we've known for a while, but first time on the program, who's the General Manager of HPC and AI, for the Lenovo Data Center Group. Scott and Kevin, thanks so much for joining us. >> It's great to be here Stu. >> Yeah, thank you. >> Alright, so Kevin, as I said, you you've been on the program a number of times, first when it was just Mellanox, now of course the networking team, there's some other acquisitions that have come in. If you could just set us up with the relationship between Nvidia and Lenovo. And there's some news today that we're here to talk about too. So let's start getting into that. And then Scott, you'll jump in after Kevin. >> Yeah, so we've been a long time partner with Lenovo, on our high performance computing. And so that's the InfiniBand piece of our business. And more and more, we're seeing that AI workloads are very, very similar to HPC workloads. And so that's been a great partnership that we've had for many, many years. And now we're expanding that, and we're launching a OEM relationship with Lenovo, for our Ethernet switches. And again, with our Ethernet switches, we really take that heritage of low latency, high performance networking that we built over many years in HPC, and we bring that to Ethernet. And of course that can be with HPC, because frequently in an HPC supercomputing environment, or in an AI supercomputing environment, you'll also have an Ethernet network, either for management, or sometimes for storage. And now we can offer that together with Lenovo. So it's a great partnership. We talked about it briefly last month, and now we're coming to market, and we'll be able to offer this to the market. >> Yeah, yeah, Kevin, we're super excited about it here in Lenovo as well. We've had a great relationship over the years with Mellanox, with Nvidia Mellanox. And this is just the next step. We've shown in HPC that the days of just taking an Ethernet card, or an InfiniBand card, plugging it in the system, and having it work properly are gone. You really need a system that's engineered for whatever task the customer is going to use. And we've known that in HPC for a long time, as we move into workloads, like artificial intelligence, where networking is a critical aspect of getting these systems to communicate with one another, and work properly together. We love from HPC perspective, to use InfiniBand, but most enterprise clients are using Ethernet. So where do we go? We go to a partner that we've trusted for a very long time. And we selected the Nvidia Mellanox Ethernet switch family. And we're really excited to be able to bring that end-to-end solution to our enterprise clients, just like we've been doing for HPC for a while. >> Yeah, well Scott, maybe if you could. I'd love to hear a little bit more about kind of that customer demand that those usages there. So you think traditionally, of course, is supercomputing, as you both talked about that move from InfiniBand, to leveraging Ethernet, is something that's been talked about for quite a while now in the industry. But maybe that AI specifically, could you talk about what are the networking requirements, how similar is it? Is it 95% of the same architecture, as what you see in HPC environments? And also, I guess the big question there is, how fast are customers adopting, and rolling out those AI solutions? And what kind of scale are they getting them to today? >> So yeah, there's a lot there of good things we can talk about. So I'd say in HPC, the thing that we've learned, is that you've got to have a fabric that's up to the task. When you're testing an HPC solution, you're not looking at a single node, you're looking at a combination of servers, and storage, management, all these things have to come together, and they come together over InfiniBand fabric. So we've got this nearly a purpose built fabric that's been fantastic for the HPC community for a long time. As we start to do some of that same type of workload, but in an enterprise environment, many of those customers are not used to InfiniBand, they're used to an Ethernet fabric, something that they've got all throughout their data center. And we want to try to find a way to do was, bring a lot of that rock solid interoperability, and pre-tested capability, and bring it to our enterprise clients for these AI workloads. Anything high performance GPUs, lots of inner internode communications, worries about traffic and congestion, abnormalities in the network that you need to spot. Those things happen quite often, when you're doing these enterprise AI solutions. You need a fabric that's able to keep up with that. And the Nvidia networking is definitely going to be able to do that for us. >> Yeah well, Kevin I heard Scott mention GPUs here. So this kind of highlights one of the reasons why we've seen Nvidia expand its networking capabilities. Could you talk a little bit about that kind of expansion, the portfolio, and how these use cases really are going to highlight what Nvidia helped bring to the market? >> Yeah, we like to really focus on accelerated computing applications. And whether those are HPC applications, or now they're becoming much more broadly adopted in the enterprise. And one of the things we've done is, tight integration at a product level, between GPUs, and the networking components in our business. Whether that's the adapters, or the DPU, the data processing unit, which we've talked about before. And now even with the switches here, with our friends at Lenovo, and really bringing that all together. But most importantly, is at a platform level. And by that I mean the software. And the enterprise here has all kinds of different verticals that are going after. And we invest heavily in the software ecosystem that's built on top of the GPU, and the networking. And by integrating all of that together on a platform, we can really accelerate the time to market for enterprises that wants to leverage these modern workloads, sort of cloud native workloads. >> Yeah, please Scott, if you have some follow up there. >> Yeah, if you don't mind Stu, I just like to say, five years ago, the roadmap that we followed was the processor roadmap. We all could tell you to the week when the next Xeon processor was going to come out. And that's what drove all of our roadmaps. Since that time what we found is that the items that are making the radical, the revolutionary improvements in performance, they're attached to the processor, but they're not the processor itself. It's things like, the GPU. It's things like that, especially networking adapters. So trying to design a platform that's solely based on a CPU, and then jam these other items on top of it. It no longer works, you have to design these systems in a holistic manner, where you're designing for the GPU, you're designing for the network. And that's the beauty of having a deep partnership, like we share with Nvidia, on both the GPU side, and on the networking side, is we can do all that upfront engineering to make sure that the platform, the systems, the solution, as a whole works exactly how the customer is going to expect it to. >> Kevin, you mentioned that a big piece of this is software now. I'm curious, there's an interesting piece that your networking team has picked up, relatively recently, that the Cumulus Linux, so help us understand how that fits into the Ethernet portfolio? And would it show up in these kind of applications that we're talking about? >> Yeah, that's a great question. So you're absolutely right, Cumulus is integral to what we're doing here with Lenovo. If you looked at the heritage that Mellanox had, and Cumulus, it's all about open networking. And what we mean by that, is we really decouple the hardware, and the software. So we support multiple network operating systems on top of our hardware. And so if it's, for example, Sonic, or if it's our Onyx or Dents, which is based on switch def. But Cumulus who we just recently acquired, has been also on that same access of open networking. And so they really support multiple platforms. Now we've added a new platform with our friends at Lenovo. And really they've adopted Cumulus. So it is very much centered on, Enterprise, and really a cloud like experience in the Enterprise, where it's Linux, but it's highly automated. Everything is operationalized and automated. And so as a result of that, you get sort of the experience of the cloud, but with the economics that you get in the Enterprise. So it's kind of the best of both worlds in terms of network analytic, and all of the ability to do things that the cloud guys are doing, but fully automated, and for an Enterprise environment. >> Yeah, so Kevin, I mean, I just want to say a few things about this. We're really excited about the Cumulus acquisition here. When we started our negotiations with Mellanox, we were still planning to use Onyx. We love Onyx, it's been our IB nodes of choice. Our users love, our are architects love it. But we were trying to lean towards a more open kind of futuristic, node as we got started with this. And Cumulus is really perfect. I mean it's a Linux open source based system. We love open source in HPC. The great thing about it is, we're going to be able to take all the great learnings that we've had with Onyx over the years, and now be able to consolidate those inside of Cumulus. We think it's the perfect way to start this relationship with Nvidia networking. >> Well Scott, help us understand a little more. What you know what does this expansion of the partnership mean? If you're talking about really the full solutions that Lenovo opens in the think agile brand, as well as the hybrid and cloud solutions. Is this something then that, is it just baked into the solution, is it a reseller, what should customers, and your your channel partners understand about this? >> Yeah, so any of the Lenovo solutions that require a switch to perform the functionality needed across the solution, are going to show up with the networking from Nvidia inside of it. Reasons for that, a couple of reasons. One is even something as simple as solution management for HPC, the switch is so integral to how we do all that, how we push all those functions down, how we deploy systems. So you've got to have a switch, in a connectivity methodology, that ensures that we know how to deploy these systems. And no matter what scale they are, from a few systems up, to literally thousands of systems, we've got something that we know how to do. Then when we're we're selling these solutions, like an SAP solution, for instance. The customer is not buying a server anymore, they're buying a solution, they're buying a functionality. And we want to be able to test that in our labs to ensure that that system, that rack, leaves our factory ready to do exactly what the customer is looking for. So any of the systems that are going to be coming from us, pre configured, pre tested, are all going to have Nvidia networking inside of them. >> Yeah, and I think that's, you mentioned the hybrid cloud. I think that's really important. That's really where we cut our teeth first in InfiniBand, but also with our Ethernet solutions. And so today, we're really driving a bunch of the big hyper scalars, as well as the big clouds. And as you see things like SAP or Azure, it's really important now that you're seeing Azure stack coming into a hybrid environment, that you have the known commodity here. So we're something that we're built in to many of those different platforms, with our Spectrum ASIC, as well as our adapters. And so now the ability with Nvidia, and Lenovo together, to bring that to enterprise customers, is really important. I think it's a proven set of components that together forms a solution. And that's the real key, as Scott said, is delivering a solution, not just piece parts, we have a platform, that software, hardware, all of it integrated. >> Well, it's great to see you. We've had an existing partnership for a while. I want to give you both the opportunity, anything specific, you've been hearing kind of the customer demand leading up this. Is it people that might be transitioning from InfiniBand to Ethernet? Or is it just general market adoption of new solutions that you have out there? (speakers talk over each other) >> You go ahead and start. >> Okay, so I think that there's different networks for different workloads, is what we've seen. And InfiniBand certainly is going to continue to be the best platform out there for HPC, and often for AI. But as Scott said, the enterprise frequently is not familiar with that, and for various reasons, would like to leverage Ethernet. So I think we'll see two different cases, one where there's Ethernet with an InfiniBand network. And the other is for new enterprise workloads that are coming, that are very AI centric, modern workloads, sort of cloud native workloads. You have all of the infrastructure in place with our Spectrum ASICs, and our Connectx adapters, and now integrated with GPUs, that we'll be able to deliver solutions rather than just compliments. And that's the key. >> Yeah, I think Stu, a great example, I think of where you need that networking, like we've been used to an HPC, is when you start looking at deep learning in training, scale out training. A lot of companies have been stuck on a single workstation, because they haven't been able to figure out how to spread that workload out, and chop it up, like we've been doing in HPC, because they've been running into networking issues. They can't run over an unoptimized network. With this new technology, we're hoping to be able to do a lot of the same things that HPC customers take for granted every day, about workload management, distribution of workload, chopping jobs up into smaller portions, and feeding them out to a cluster. We're hoping that we're going to be able to do those exact same things for our enterprise clients. And it's going to look magical to them, but it's the same kind of thing we've been doing forever. With Mellanox, in the past, now Nvidia networking, we're just going to take that to the enterprise. I'm really excited about it. >> Well, it's so much flexibility. We used to look at, it would take a decade to roll out some new generations. Kevin, if you could just give us latest speeds and feeds. If I look at Ethernet, did I see that this has from n gig, all the way up to 400 gig? I think I lose track a little bit of some of the pieces. I know the industry as a whole is driving it. But where are we with the general customer adoption of some of the some of the speeds today? >> Yeah indeed, we're coming up on the 40th anniversary of the first specification of Ethernet. And we're about 4000 times faster now, 40,000 times faster at 400 gigabits, versus 10 megabits. So yeah, we're shipping today at the adapter level, 100 gig, and even 200 gig. And then at the switch level, 400 gig. And people sort of ask, "Do we really need all that performance?" The answer is absolutely. So the amount of data that the GPU can crunch, and these AI workloads, these giant neural networks, it needs massive amounts of data. And then as you're scaling out, as Scott was talking about, much along the lines of InfiniBand Ethernet needs that same level of performance, throughput, latency and offloads, and we're able to deliver. >> Yeah, so Kevin, thank you so much. Scott, I want to give you a final word here. Anything else you want your customers to understand regarding this partnerships? >> Yeah, just a quick one Stu, quick one. So we've been really fortunate in working really closely with Mellanox over the years, and with Nvidia. And now the two together, we're just excited about what the future holds. We've done some really neat things in HPC, with being one of the first watercool an InfiniBand card. We're one of the first companies to deploy Dragonfly topology. We've done some unique things where we can share a single IP adapter, across multiple users. We're looking forward to doing a lot of that same exact kind of innovation, inside of our systems as we look to Ethernet. We often think that as speeds of Ethernet continue to go higher, we may see more and more people move from InfiniBand to Ethernet. I think that now having both of these offerings inside of our lineup, is going to make it really easy for customers to choose what's best for them over time. So I'm excited about the future. >> Alright, well Kevin and Scott, thank you so much. Deep integration and customer choice, important stuff. Thank you so much for joining us. >> Thank you Stu. >> Thanks Stu. >> Alright, I'm Stu Miniman, and thank you. Thanks for watching theCUBE. (upbeat music)

>> Narrator: From around the global its theCUBE, covering HPE Discover Virtual Experience, brought to you by HPE. >> Hi, I'm Stu Miniman and this is theCUBE's coverage of HPE, discover the virtual experience for 2020, getting to talk to Hp executives, their partners, the ecosystem, where they are around the globe, this session we're going to be digging in about artificial intelligence, obviously a super important topic these days. And to help me do that, I've got two guests from Nvidia, sitting in the window next to me, we have Paresh Kharya, he's director of product marketing and sitting next to him in the virtual environment is Kevin Deierling, who is this senior vice president of marketing as I mentioned both with Nvidia. Thank you both so much for joining us. >> Thank you, so great to be here. >> Great to be here. >> All right, so Paresh when you set the stage for us? AI, obviously, one of those mega trends to talk about but just, give us the stages, where Nvidia sits, where the market is, and your customers today, that they think about AI. >> Yeah, so we are basically witnessing a massive changes that are happening across every industry. And it's basically the confluence of three things. One is of course, AI, the second is 5G and IOT, and the third is the ability to process all of the data that we have, that's now possible. For AI we are now seeing really advanced models, from computer vision, to understanding natural language, to the ability to speak in conversational terms. In terms of IOT and 5G, there are billions of devices that are sensing and inferring information. And now we have the ability to act, make decisions in various industries, and finally all of the processing capabilities that we have today, at the data center, and in the cloud, as well as at the edge with the GPUs as well as advanced networking that's available, we can now make sense all of this data to help industrial transformation. >> Yeah, Kevin, you know it's interesting when you look at some of these waves of technology and we say, "Okay, there's a lot of new pieces here." You talk about 5G, it's the next generation but architecturally some of these things remind us of the past. So when I look at some of these architectures, I think about, what we've done for high performance computing for a long time, obviously, you know, Mellanox, where you came from through NVIDIA's acquisition, strong play in that environment. So, maybe give us a little bit compare, contrast, what's the same, and what's different about this highly distributed, edge compute AI, IOT environment and what's the same with what we were doing with HPC in the past. >> Yeah, so we've--Mellanox has now been a part of Nvidia for a little over a month and it's great to be part of that. We were both focused on accelerated computing and high performance computing. And to do that, what it means is the scale and the type of problems that we're trying to solve are just simply too large to fit into a single computer. So if that's the case, then you connect a lot of computers. And Jensen talked about this recently at the GTC keynote where he said that the new unit computing, it's really the data center. So it's no longer the box that sits on your desk or even in Iraq, it's the entire data center because that's the scale of the types of problems that we're solving. And so the notion of scale up and scale out, the network becomes really, really critical. And we're doing high-performance networking for a long time. When you move to the edge, instead of having, a single data center with 10,000 computers, you have 10,000 data centers, each of which as a small number of servers that is processing all of that information that's coming in. But in a sense, the problems are very, very similar, whether you're at the edge or you're doing massive HPC, scientific computing or cloud computing. And so we're excited to be part of bringing together the AI and the networking because they are really optimizing at the data center scale across the entire stack. >> All right, so it's interesting. You mentioned, Nvidia CEO, Jensen. I believe if I saw right in there, he actually could, wrote a term which I had not run across, it was the data processing unit or DPU in that, data center, as you talked about. Help us wrap our heads around this a little bit. I know my CPU, when I think about GPUs, I obviously think of Nvidia. TPUs, in the cloud and everything we're doing. So, what is DPUs? Is this just some new AI thing or, is this kind of a new architectural model? >> Yeah. I think what Jensen highlighted is that there's three key elements of this accelerated disaggregated infrastructure that the data center has becoming. And so that's the CPU, which is doing traditional single threaded workloads but for all of the accelerated workloads, you need the GPU. And that does massive parallelism deals with massive amounts of data, but to get that data into the GPU and also into the CPU, you need really an intelligent data processing because the scale and scope of GPUs and CPUs today, these are not single core entities. These are hundreds or even thousands of cores in a big system. And you need to steer the traffic exactly to the right place. You need to do it securely. You need to do it virtualized. You need to do it with containers and to do all of that, you need a programmable data processing unit. So we have something called our BlueField, which combines our latest, greatest, 100 gig and 200 gig network connectivity with Arm processors and a whole bunch of accelerators for security, for virtualization, for storage. And all of those things then feed these giant parallel engines which are the GPU. And of course the CPU, which is really the workload at the application layer for non-accelerated outs. >> Great, so Paresh, Kevin talked about, needing similar types of services, wherever the data is. I was wondering if you could really help expand for us a little bit, the implications of it AI at the edge. >> Sure, yeah, so AI is basically not just one workload. AI is many different types of models and AI also means training as well as inferences, which are very different workloads or AI printing, for example, we are seeing the models growing exponentially, think of any AI model, like a brain of a computer or like a brain, solving a particular use case a for simple models like computer vision, we have models that are smaller, bugs have computer vision but advanced models like natural language processing, they require larger brains or larger models, so on one hand we are seeing the size of the AI models increasing tremendously and in order to train these models, you need to look at computing at the scale of data center, many processors, many different servers working together to train a single model, on the other hand because of these AI models, they are so accurate today from understanding languages to speaking languages, to providing the right recommendations whether it's for products or for content that you may want to consume or advertisements and so on. These models are so effective and efficient that they are being powered by AI today. These applications are being powered by AI and each application requires a small amount of acceleration, so you need the ability to scale out or, and support many different applications. So with our newly launched MPR architecture, just couple of weeks to go that Jensen announced, in the virtual keynote for the first time, we are now able to provide both, scale up and scale out both training data analytics as well as imprints on the single architecture and that's very exciting. >> Yeah, so look at that. The other thing that's interesting is you're talking about at the edge and scale out versus scale up, the networking is critical for both of those. And there's a lot of different workloads. And as Paresh was describing, you've got different workloads that require different amounts of GPU or storage or networking. And so part of that vision of this data center as the computer is that, the DPU lets you scale independently, everything. So you can compose, you desegregate into DPUs and storage and CPUs, and then you compose exactly the computer that you need on the fly container, right, to solve the problem that you're solving right now. So these new way of programming is programming the entire data center at once and you'll go grab all of it and it'll run for a few hundred milliseconds even and then it'll come back down and recompose itself onsite. And to do that, you need this very highly efficient networking infrastructure. And the good news is we're here at HPE Discover. We've got a great partner with HPE. You know, they have our M series switches that uses the Mellanox hundred gig and now even 200 and 400 gig ethernet switches, we have all of our adapters and they have great platforms. The Apollo platform for example, is break for HPC and they have other great platforms that we're looking at with the new telco that we're doing or 5G and accelerating that. >> Yeah, and on the edge computing side, there's the edge line set of products which are very interesting, the other sort of aspect that I wanted to touch upon, is the whole software stack that's needed for the edge. So edge is different in the sense that it's not centrally managed, the edge computing devices are distributed remote locations. And so managing the workflow of running and updating software on it is important and needs to be done in a very secure manner. The second thing that's, that's very different again, for the edges, these devices are going to require connectivity. As Kevin was pointing out, the importance of networking so we also announced, a couple of weeks ago at our GTC, our EGX product that combines the Mellanox NIC and our GPUs into a single a processor, Mellanox NIC provides a fast connectivity, security, as well as the encryption and decryption capabilities, GPUs provide acceleration to run the advanced DI models, that are required for applications at the edge. >> Okay, and if I understood that, right. So, you've got these throughout the HPE the product line, HPE's got long history of making, flexible configurations, I remember when they first came out with a Blade server it was, different form factors, different connectivity options, they pushed heavily into composable infrastructure. So it sounds like this is just a kind of extending, you know, what HP has been doing for a couple of decades. >> Yeah, I think HP is a great partner there and these new platforms, the EGX, for example that was just announced, a great workload there is a 5G telco. So we'll be working with our friends at HPE to take that to market as well. And, you know, really, there's a lot of different workloads and they've got a great portfolio of products across the spectrum from regular servers. And 1U, 2U, and then all the way up to their big Apollo platform. >> Well I'm glad you brought up telco, I'm curious, are there any specific, applications or workloads that, where the low hanging fruit or the kind of the first targets that you use for AI acceleration? >> Yeah, so you know, the 5G workload is just awesome. We're introduced with the EGX, a new platform called Ariel which is a programming framework and there were lots of partners there that were part of that, including, folks like Ericsson. And the idea there is that you have a software defined hardware accelerated radio area network, so a cloud RAM and it really has all of the right attributes of the cloud and what's nice there is now you can change on the fly, the algorithms that you're using for the baseband codex without having to go climb a radio tower and change the actual physical infrastructure. So that's a critical part. Our role in that, on the networking side, we introduced the technology that's part of EGX then are connected, It's like the DX adapter, it's called 5T for 5G. And one of the things that happens is you need this time triggered transport or a telco technology. That's the 5T's for 5G. And the reason is because you're doing distributed baseband unit, distributed radio processing and the timing between each of those server nodes needs to be super precise, 20 nanosecond. It's something that simply can't be done in software. And so we did that in hardware. So instead of having an expensive FPGA, I try to synchronize all of these boxes together. We put it into our NIC and now we put that into industry standard servers HP has some fantastic servers. And then with the EGX platform, with that we can build, really scale out software to client cloud RAM. >> Awesome, Paresh, anything else on the application side you'd like to add in just about what Kevin spoke about. >> Oh yeah, so from application perspective, every industry has applications that touch on edge. If you take a look at the retail, for example, there is, you know, all the way from supply chain to inventory management, to keeping the right stock units in the shelves, making sure there is a there is no slippage or shrinkage. So to telecom, to healthcare, we are re-looking at constantly monitoring patients and taking actions for the best outcomes to manufacturing. We are looking to automate production detecting failures much early on in the production cycle and so on every industry has different applications but they all use AI. They can all leverage the computing capabilities and high-speed networking at the edge to transform their business processes. >> All right, well, it's interesting almost every time we've talked about AI, networking has come up. So, you know, Kevin, I think that probably ease up a little bit why, Nvidia, spent around $7 billion for the acquisition of Mellanox and not only was it the Mellanox acquisition, Cumulus Networks, very known in the network space for software defined really, operating system for networking but give us strategically, does this change the direction of Nvidia, how should we be thinking about Nvidia in the overall network? >> Yeah, I think the way to think about it is going back to that data center as the computer. And if you're thinking about the data center as computer then networking becomes the back plane, if you will of that data center computer and having a high performance network is really critical. And Mellanox has been a leader in that for 20 years now with our InfiniBand and our Ethernet product. But beyond that, you need a programmatic interface because one of the things that's really important in the cloud is that everything is software defined and it's containerized now and there is no better company in the world then Cumulus, really the pioneer and building Cumulus clinics, taking the Linux operating system and running that on multiple homes. So not just hardware from Mellanox but hardware from other people as well. And so that whole notion of an open networking platform more committed to, you need to support that and now you have a programmatic interface that you can drop containers on top of, Cumulus has been the leader in the Linux FRR, it's Free Range Routing, which is the core routing algorithm. And that really is at the heart of other open source network operating systems like Sonic and DENT so we see a lot of synergy here, all the analytics that Cumulus is bringing to bear with NetQ. So it's really great that they're going to be part here of the Nvidia team. >> Excellent, well thank you both much. Want to give you the final word, what should they do, HPE customers in their ecosystem know about the Nvidia and HPE partnership? >> Yeah, so I'll start you know, I think HPE has been a longtime partner and a customer of ours. If you have accelerated workloads, you need to connect those together. The HPE server portfolio is an ideal place. We can combine some of the work we're doing with our new amp years and existing GPUs and then also to connect those together with the M series, which is their internet switches that are based on our spectrum switch platforms and then all of the HPC related activities on InfiniBand, they're a great partner there. And so all of that, pulling it together, and now as at the edge, as edge becomes more and more important, security becomes more and more important and you have to go to this zero trust model, if you plug in a camera that's somebody has at the edge, even if it's on a car, you can't trust it. So everything has to become, validated authenticated, all the data needs to be encrypted. And so they're going to be a great partner because they've been a leader and building the most secure platforms in the world. >> Yeah and on the data center, server, portfolio side, we really work very closely with HP on various different lines of products and really fantastic servers from the Apollo line of a scale up servers to synergy and ProLiant line, as well as the Edgeline for the edge and on the super computing side with the pre side of things. So we really work to the fullest spectram of solutions with HP. We also work on the software side, wehere a lot of these servers, are also certified to run a full stack under a program that we call NGC-Ready so customers get phenomenal value right off the bat, they're guaranteed, to have accelerated workloads work well when they choose these servers. >> Awesome, well, thank you both for giving us the updates, lots happening, obviously in the AI space. Appreciate all the updates. >> Thanks Stu, great to talk to you, stay well. >> Thanks Stu, take care. >> All right, stay with us for lots more from HPE Discover Virtual Experience 2020. I'm Stu Miniman and thank you for watching theCUBE. (bright upbeat music)

>>Live from San Diego, California It's the Q covering Koopa and Cloud Native Cot brought to you by Red Cloud, Native Computing Pounding and its ecosystem March. >>Welcome back to the Cube here at Q. Khan Club native Khan, 2019 in San Diego, California Instrumental in my co host is Jon Cryer and first of all, happy to welcome back to the program. Diane Mueller, who is the technical of the tech lead of cloud native technology. I'm sorry. I'm getting the wrong That's director of community development Red Hat, because renew. Goodbye is the technical lead of cognitive technologies at in video game to the end of day one. I've got three days. I gotta make sure >>you get a little more Red Bull in the conversation. >>All right, well, there's definitely a lot of energy. Most people we don't even need Red Bull here because we're a day one. But Diane, we're going to start a day zero. So, you know, you know, you've got a good group of community of geeks when they're like Oh, yeah, let me fly in a day early and do like 1/2 day or full day of deep dives. There So the Red Hat team decided to bring everybody on a boat, I guess. >>Yeah. So, um, open ships Commons gathering for this coup con we hosted at on the inspiration Hornblower. We had about 560 people on a boat. I promised them that it wouldn't leave the dock, but we deal still have a little bit of that weight going on every time one of the big military boats came by. And so people were like a little, you know, by the end of the day, but from 8 a.m. in the morning till 8 p.m. In the evening, we just gathered had some amazing deep dives. There was unbelievable conversations onstage offstage on we had, ah, wonderful conversation with some of the new Dev ops folks that have just come on board. That's a metaphor for navigation and Coop gone. And and for events, you know, Andrew Cliche for John Willis, the inevitable Crispin Ella, who runs Open Innovation Labs, and J Bloom have all just formed the global Transformation Office. I love that title on dhe. They're gonna be helping Thio preach the gospel of Cultural Dev ops and agile transformation from a red hat office From now going on, there was a wonderful conversation. I felt privileged to actually get to moderate it and then just amazing people coming forward and sharing their stories. It was a great session. Steve Dake, who's with IBM doing all the SDO stuff? Did you know I've never seen SDO done so well, Deployment explains so well and all of the contents gonna be recorded and up on Aaron. We streamed it live on Facebook. But I'm still, like reeling from the amount of information overload. And I think that's the nice thing about doing a day zero event is that it's a smaller group of people. So we had 600 people register, but I think was 560 something. People show up and we got that facial recognition so that now when they're traveling through the hallways here with 12,000 other people, that go Oh, you were in the room. I met you there. And that's really the whole purpose for comments. Events? >>Yeah, I tell you, this is definitely one of those shows that it doesn't take long where I say, Hey, my brain is full. Can I go home. Now. You know I love your first impressions of Q Khan. Did you get to go to the day zero event And, uh, what sort of things have you been seeing? So >>I've been mostly I went to the lightning talks, which were amazing. Anything? Definitely. There. A number of shout outs to the GPU one, of course. Uh, friend in video. But I definitely enjoyed, for example, of the amazing D. M s one, the one about operators. And generally all of them were very high quality. >>Is this your first Q? Khan, >>I've been there. I've been a year. This is my third con. I've been accused in Europe in the past. Send you an >>old hat old hand at this. Well, before we get into the operator framework and I wanna love to dig into this, I just wanted to ask one more thought. Thought about open shift, Commons, The Commons in general, the relationship between open shift, the the offering. And then Okay, the comments and okay, D and then maybe the announcement about about Okay. Dee da da i o >>s. Oh, a couple of things happened yesterday. Yesterday we dropped. Okay, D for the Alfa release. So anyone who wants to test that out and try it out it's an all operators based a deployment of open shift, which is what open ship for is. It's all a slightly new architectural deployment methodology based on the operator framework, and we've been working very diligently. Thio populate operator hub dot io, which is where all of the upstream projects that have operators like the one that Reynolds has created for in the videos GP use are being hosted so that anyone could deploy them, whether on open shift or any kubernetes so that that dropped. And yesterday we dropped um, and announced Open Sourcing Quay as project quay dot io. So there's a lot of Io is going on here, but project dia dot io is, um, it's a fulfillment, really, of a commitment by Red Hat that whenever we do an acquisition and the poor folks have been their acquired by Cora West's and Cora Weston acquired by Red Hat in an IBM there. And so in the interim, they've been diligently working away to make the code available as open source. And that hit last week and, um, to some really interesting and users that are coming up and now looking forward to having them to contribute to that project as well. But I think the operator framework really has been a big thing that we've been really hearing, getting a lot of uptake on. It's been the new pattern for deploying applications or service is on getting things beyond just a basic install of a service on open shift or any kubernetes. And that's really where one of the exciting things yesterday on we were talking, you know, and I were talking about this earlier was that Exxon Mobil sent a data scientist to the open ship Commons, Audrey Resnick, who gave this amazing presentation about Jupiter Hub, deeper notebooks, deploying them and how like open shift and the advent of operators for things like GP use is really helping them enable data scientists to do their work. Because a lot of the stuff that data signs it's do is almost disposable. They'll run an experiment. Maybe they don't get the result they want, and then it just goes away, which is perfect for a kubernetes workload. But there are other things you need, like a Jeep use and work that video has been doing to enable that on open shift has been just really very helpful. And it was It was a great talk, but we were talking about it from the first day. Signs don't want to know anything about what's under the hood. They just want to run their experiments. So, >>you know, let's like to understand how you got involved in the creation of the operator. >>So generally, if we take a step back and look a bit at what we're trying to do is with a I am l and generally like EJ infrastructure and five G. We're seeing a lot of people. They're trying to build and run applications. Whether it's in data Center at the and we're trying to do here with this operator is to bring GPS to enterprise communities. And this is what we're working with. Red Hat. And this is where, for example, things like the op Agrestic A helps us a lot. So what we've built is this video Gee, few operator that space on the upper air sdk where it wants us to multiple phases to in the first space, for example, install all the components that a data scientist were generally a GPU cluster of might want to need. Whether it's the NVIDIA driver, the container runtime, the community's device again feast do is as you go on and build an infrastructure. You want to be able to have the automation that is here and, more importantly, the update part. So being able to update your different components, face three is generally being able to have a life cycle. So as you manage multiple machines, these are going to get into different states. Some of them are gonna fail, being able to get from these bad states to good states. How do you recover from them? It's super helpful. And then last one is monitoring, which is being able to actually given sites dr users. So the upper here is decay has helped us a lot here, just laying out these different state slips. And in a way, it's done the same thing as what we're trying to do for our customers. The different data scientists, which is basically get out of our way and allow us to focus on core business value. So the operator, who basically takes care of things that are pretty cool as an engineer I lost due to your election. But it doesn't really help me to focus on like my core business value. How do I do with the updates, >>you know? Can I step back one second, maybe go up a level? The problem here is that each physical machine has only ah limited number of NVIDIA. GPU is there and you've got a bunch of containers that maybe spawning on different machines. And so they have to figure out, Do I have a GPU? Can I grab one? And if I'm using it, I assume I have to reserve it and other people can't use and then I have to give it up. Is that is that the problem we're solving here? So this is >>a problem that we've worked with communities community so that like the whole resource management, it's something that is integrated almost first class, citizen in communities, being able to advertise the number of deep, use their your cluster and used and then being able to actually run or schedule these containers. The interesting components that were also recently added are, for example, the monitoring being able to see that a specific Jupiter notebook is using this much of GP utilization. So these air supercool like features that have been coming in the past two years in communities and which red hat has been super helpful, at least in these discussions pushing these different features forward so that we see better enterprise support. Yeah, >>I think the thing with with operators and the operator lifecycle management part of it is really trying to get to Day two. So lots of different methodologies, whether it's danceable or python or job or or UH, that's helm or anything else that can get you an insult of a service or an application or something. And in Stan, she ate it. But and the operator and we support all of that with SD case to help people. But what we're trying to do is bridge the to this day to stuff So Thea, you know, to get people to auto pilot, you know, and there's a whole capacity maturity model that if you go to operator hab dot io, you can see different operators are a different stages of the game. So it's been it's been interesting to work with people to see Theo ah ha moment when they realize Oh, I could do this and then I can walk away. And then if that pod that cluster dies, it'll just you know, I love the word automatically, but they, you know, it's really the goal is to help alleviate the hands on part of Day two and get more automation into the service's and applications we deploy >>right and when they when they this is created. Of course it works well with open shift, but it also works for any kubernetes >>correct operator. HAB Daddio. Everything in there runs on any kubernetes, and that's really the goal is to be ableto take stuff in a hybrid cloud model. You want to be able to run it anywhere you want, so we want people to be unable to do it anywhere. >>So if this really should be an enabler for everything that it's Vinny has been doing to be fully cloud native, Yes, >>I think completely arable here is this is a new attack. Of course, this is a bit there's a lot of complexity, and this is where we're working towards is reducing the complexity and making true that people there. Dan did that a scientist air machine learning engineers are able to focus on their core business. >>You watch all of the different service is in the different things that the data scientists are using. They don't I really want to know what's under under the hood. They would like to just open up a Jupiter Hub notebook, have everything there. They need, train their models, have them run. And then after they're done, they're done and it goes away. And hopefully they remember to turn off the Jeep, use in the woods or wherever it is, and they don't keep getting billed for it. But that's the real beauty of it is that they don't have to worry so much anymore about that. And we've got a whole nice life cycle with source to image or us to I. And they could just quickly build on deploy its been, you know, it's near and dear to my heart, the machine learning the eyesight of stuff. It is one of the more interesting, you know, it's the catchy thing, but the work was, but people are really doing it today, and it's been we had 23 weeks ago in San Francisco, we had a whole open ship comments gathering just on a I and ML and you know, it was amazing to hear. I think that's the most redeeming thing or most rewarding thing rather for people who are working on Kubernetes is to have the folks who are doing workloads come and say, Wow, you know, this is what we're doing because we don't get to see that all the time. And it was pretty amazing. And it's been, you know, makes it all worthwhile. So >>Diane Renaud, thank you so much for the update. Congratulations on the launch of the operators and look forward to hearing more in the future. >>All right >>to >>be here >>for John Troy runs to minimum. More coverage here from Q. Khan Club native Khan, 2019. Thanks for watching. Thank you.

>> from Austin, Texas. It's Theo Cube, covering pure storage. Accelerate 2019. Brought to you by pure storage. >> Welcome to the Cube. The leader in live tech coverage covering up your accelerate 2019. Lisa Martin with Dave Ilan in Austin, Texas, this year. Pleased to welcome a couple of guests to the program. Please meet Charlie Boyle, VP and GM of DJ X Systems at N Video. Hey, Charlie, welcome back to the Cube, but in a long time ago and we have Brian Schwartz, VP of product management and development at your brain. Welcome. >> Thanks for having me. >> Here we are Day one of the event. Lots of News This morning here is just about to celebrate its 10th anniversary. A lot of innovation and 10 years. Nvidia partnerships. About two is two and 1/2 years old or so. Brian, let's start with you. Give us a little bit of an overview about where pure and and video are, and then let's dig into this news about the Aye aye data hub. >> Cool, it's It's been a good partnership for a couple of years now, and it really was born out of work with mutual customers. You know we brought out the flash blade product, obviously in video was in the market with DJ X is for a I, and we really started to see overlap in a bunch of initial deployments. And we really realized that there was a lot of wisdom to be gained off some of these early I deployments of capturing some of that knowledge and wisdom from those early practitioners and being able to share it with the with the wider community. So that's really kind of where the partnership was born going for a couple of years now, I've got a couple of chapters behind us and many more in the future. And obviously the eye data hub is the piece that we really talked about at this year's accelerate. >> Yeah, areas about been in the market for what? About a year and 1/2 or so Almost >> two years. >> Two years? All right, tell us a little bit about the adoption. What what customers were able to dio with this a ready infrastructure >> and point out the reason we started the partnership was our early customers that were buying dejected product from us. They were buying pure stored. Both leaders and high performance. And as they were trying to put them together, they're like, How should we do this? What's the optimal settings? They've been using storage for years. I was kind of new to them and they needed that recipe. So that's, you know, the early customer experiences turned into airy the solution, and, you know, the whole point of this to simplify. I sounds kind of scary to a lot of folks and the data scientists really just need to be productive. They don't care about infrastructure, but I t s to support this. So I t was very familiar with pure storage. They used them for years for high performance data and as they brought in the Nvidia Compute toe work with that, you know, having a solution that we both supported was super important to the I T practitioners because they knew it worked. They knew we both supported it. We stood behind it and they could get up and running in a matter of days or weeks versus 6 to 9 months if they built it >> themselves. >> You look at companies that you talk to customers. Let's let's narrow it down to those that have data scientists least one day to scientists and ask him where they are in their maturity model, if one is planning to was early threes, they got multiple use cases and four is their enterprise wide. How do you see the landscape? Are you seeing pretty aggressive adoption in those as I couched it, or is it still early? >> I mean so every customers in a different point. So there's definitely a lot of people that are still early, but we've seen a lot of production use cases. You know, everyone talks about self driving cars, but that's, you know, there's a lot behind that. But real world use cases say medicals got a ton? You know, we've got partner companies that you are looking at a reconstruction of MRI's and CT scans cutting the scan time down by 75%. You know, that's real patient outcome. You know, we've got industrial inspection, we're in Texas. People fly drones around and have a eye. Models that are built in their data center on the drone and the field operators get to re program the drones based on what they see and what is happening. Real time and re trains every night. So depending on the industry really depends on where people are in the maturity her. But you know, really, our message out to the enterprises are start now. You know, whether you've got one data scientist, you've got some community data scientists. There's no reason to wait on a because there's a use case that work somewhere in your inner. >> So so one of the key considerations to getting started. What would you say? >> So one thing I would say is, look any to your stages of maturity. Any good investment is done through some creation of business value, right? And an understanding of kind of what problem you're trying to solve and making sure it's compelling. Problem is an important one, and some industries air farther along. Like you know, one of the ones that most everybody's familiar with is the tech industry itself. Every recommendation engine you've probably ever seen on the Internet is backed by some form of a I behind it because they wanted to be super fast and, you know, customized to you as a user. So I think understanding the business value creation problem is is a really important step of it and many people go through an early stage of experimentation, data modeling really kind of, say, a prototyping stage before they go into a mass production use case. It's a very classic i t adoption curve. Just add a comment to the earlier kind of trend is it's a megatrend. Yes, not everybody is doing it in massive wide scale production today. There's some industries that are farther ahead. If you look forward over the next 15 to 20 years, there's a massive amount of Ai ai coming, and it's a It is a new form of computing, the GPU driven computing and the whole point about areas getting the ingredients right. Thio have this new set of infrastructure have storage network compute on the software stack all kind of package together to make it easier to adopt, to allow people to adopt it faster because some industries are far along and others are still in the earlier stages, >> right? So how do you help for those customers and industries that aren't self driving cards of the drones that you talked about where we use case, we all understand it and are excited about it. But for other customers in different industries. How do you help them even understand the A pipeline? And where did they start? I'm sure that varies very >> a lot. But, you know, the key point is starting a I project. You have a desired outcome from Not everything's gonna be successful, but you know Aye, aye. Projects aren't something that it's not a six month I t project or a big you know, C r m. Refresh it. Something that you could take One of our classes that we have, we do a lot of end user customer training are Deep Learning Institute. You can take 1/2 day class and actually do a deep learning project that day. And so a lot of it is understanding your data, you know, and that's where your and the data hub comes in, understanding the data that you have and then formulating a question like, What could I do if I knew this thing? That's all about a I and deep learning. It's coming up with insights that aren't natural. When you just stare at the data, how can the system understand what you want? And then what are the things that you didn't expect defined that A. I is showing you about your data, and that's really a lot of where the business value comes. And how do you know more about your customer? How do you help that customer better, eh? I can unlock things that you may not have pondered yourself. >> The other thing. I'm a huge fan of analogies when you're trying to describe a new concept of people. And there's a good analogy about Ai ai data pipelines that predates, Aye aye around data warehousing like there's been industry around, extract transformers load E T L Systems for a very long period of time. It's a very common thing for many, many people in the I T industry, and I do think there's when you think about a pipeline in a I pipeline. There's an analogy there, which you have data coming in ingress data. You're cleansing it, you're cleaning it. You're essentially trying to get some value out of it. How you do that in a eyes quite a bit different, cause it's GP use and you're looking, you know, for turning unstructured data into more structure date. It's a little different than data. Warehousing traditionally was running reports, but there's a big analogy, I think, to be used about a pipeline that is familiar to people as a way to understand the new concept. >> So that's good. I like the pipeline concept. One of the one of the counters to that would be that you know, when you think about e. T ells complicated process enterprise data warehouses that were cumbersome Do you feel like automation in the A I Pipeline? When we look back 10 years from now, we'll have maybe better things to say than we do about E D W A R e g l. >> And I think one of the things that we've seen, You know, obviously we've done a ton of work in traditional. Aye, aye, But we've also done a lot in accelerated machine learning because that's a little closer to your traditional Data analytics and one of the biggest kind of ah ha moments that I've seen customers in the past year or so. It's just how quickly, by using GPU computing, they can actually look at their data, do something useful with it, and then move on to the next thing so that rapid experimentation is all you know, what a I is about. It's not a eyes, not a one and done thing. Lots of people think Oh, I have to have a recommend er engine. And then I'm done. No, you have to keep retraining it day in and day out so that it gets better. And that's before you had accelerated. Aye, aye pipeline. Before you had accelerated data pipelines that we've been doing with cheap use. It just took too long so people didn't run those experiments. Now we're seeing people exploring Maur trying different things because when your experiment takes 10 minutes, two minutes versus two days or 10 days, you can try out your cycle time. Shorter businesses could doom or and sure, you're gonna discard a lot of results. But you're gonna find those hidden gems that weren't possible before because you just didn't have the time to do >> it. Isn't a key operational izing it as well? I mean again, one of the challenges with the analogy that you gave a needy W is fine reporting. You can operationalize it for reporting, and but the use cases weren't is rich robust, and I feel as though machine intelligence is I mean, you're not gonna help but run into it. It's gonna be part of your everyday life, your thoughts. >> It's definitely part of our everyday lives. When you talk about, you know, consumer applications of everything we all use every day just don't know it's it's, you know, the voice recognition system getting your answer right the first time. You know there's a huge investments in natural language speech right now to the point that you can ask your phone a question. It's going through searching the Web for you, getting the right answer, combining that answer, reading it back to you and giving you the Web page all in less than a second. You know, before you know that be like you talked to an I. V R system. Wait, then you go to an operator. Now people are getting such a better user experience out of a I back systems that, you know over the next few years, I think end users will start preferring to deal with those based systems rather than waiting on line for human, because it'll just get it right. It'll get you the answer you need and you're done. You save time. The company save time and you've got a better outcome. >> So there's definitely some barriers to adoption skills. Is one obvious one the other. And I wonder if Puritan video attack this problem. I'm sure you have, but I'd like some color on it. His traditional companies, which a lot of your customers, their data is in pockets. It's not at the core. You look at the aye aye leaders, you know, the Big Five data their data cos it's at the core. They're applying machine intelligence to that data. How has this modern storage that we heard about this morning affected that customers abilities to really put data at their core? >> You know, it's It's a great question, Dave and I think one of the real opportunities, particularly with Flash, is to consolidate data into a smaller number off larger kind of islands of data, because that's where you could really drive the insights. And historically, in a district in world, you would never try to consolidate your data because there was too many bad performance implications of trying to do that. So people had all these pockets, and even if you could, you probably wouldn't actually want to put the date on the same system at the same time. The difference with flashes as so much performance at the at the core of it at the foundation of it. So the concept of having a very large scale system, like 150 blade system we announced this morning is a way to put a lot of the year and be able to access it. And to Charlie's point, a lot of people they're doing constant experiment, experimentation and modeling of the data. You don't know that how the date is gonna be consumed and you need a very fast kind of wide platform to do that, Which is why it's been a good fit for us to work together >> now fall upon that. Dated by its very nature. However, Brian is distributed and we heard this morning is you're attacking that problem through in a P I framework that you don't care where it is. Cloud on Prem hybrid edge. At some point in time, your thoughts on that >> well, in again the data t be used for a I I wouldn't say it's gonna be every single piece of data inside an organization is gonna be put into the eye pipeline in a lot of cases, you could break it down again. Thio What is the problem? I'm trying to solve the business value and what is the type of data that's gonna be the best fit for it? There are a lot of common patterns for consumption in a I AA speech recognition image recognition places where you have a lot of unstructured data or it's unstructured to a computer. It's not unstructured to you. When you look at a picture, you see a lot of things in it that a computer can't see right, because you recognize what the patterns are and the whole point about a eyes. It's gonna help us get structure out of these unstructured data sets so the computer can recognize more things. You know, the speech and emotions that we as humans just take for granted. It's about having computers, being able to process and respond to that in a way that they're not really people doing today. >> Hot dog, not a hot dog. Silicon Valley >> Street light. Which one of these is not a street lights and prove you're not about to ask you about distributed environments. You know customers have so much choice for everything these days on Prem hosted SAS Public Cloud. What are some of the trends that you're seeing? I always thought that to really be able to extract a tremendous amount of value from data and to deliver a I from it you needed the cloud because you needed a massive volumes of data. Appears legacy of on print. What are some of the things that you're seeing there and how is and video you're coming together to help customers wherever this data is to really dry Valley business value from these workloads, >> I have to put comments and I'll turn over to Charlie. So one is we get asked this question a lot. Like where should I run my eye? The first thing I always tell people is, Where's your data? Gravity moving these days? That's a very large tens of terror by its hundreds of terabytes petabytes of data moving very large. That's the data is actually still ah, hard challenge today. So running your A II where your date is being generated is a good first principle. And for a lot of folks they still have a lot on premise data. That's where their systems are they're generating the systems, or it's a consolidation point from the edge or other other opportunities to run it there. So that's where your date is. Run your A I there. The second thing is about giving people flexibility. We've both made pretty big investments in the world of containerized software applications. Those things are things that can run on grammar in the cloud. So trying to use a consistent set of infrastructure and software and tooling that allows people to migrate and change over time, I think, is an important strategy not only for us but also for the end users that gives them flexibility. >> So, ideally, on Prem versus Cloud implementations shouldn't be. That shouldn't be different. Be great. It would be identical. But are they today? >> So at the lowest level, there's always technical differences, but at the layers that customers are using it, we run one software stack no matter where you're running. So if it's on one of our combined R E systems, whether it's in a cloud provider, it's the same in video software stack from our lowest end consumer of rage. He views, too. The big £350 dejected too you see back there? You know, we've got one software stack runs everywhere, And when the riders making you know, it's really Renee I where your data is And while a lot of people, if you are cloud native company, if you started that way, I'm gonna tell you to run in the cloud all day long. But most enterprises, they're some of their most valuable data is still sitting on premise. They've got decades of customer experience. They've got decades of product information that's all running in systems on Prem. And when you look at speech, speech is the biggest thing you know. They've got, you know, years of call center data that's all sitting in some offline record. What am I gonna do with that? That stuff's not in the cloud. And so you want to move the processing to that because it's impossible to move that data somewhere else and transform it because you're only gonna actually use a small fraction of that data to produce your model. But at the same time, you don't want to spend a year moving that data somewhere to process it back the truck up, put some DJ X is in front of it. And you're good to go. >> Someone's gonna beat you to finding those insides. Right? So there is no time. >> So you have another question. >> I have the last question. So you got >> so in video, you gotta be Switzerland in this game. So I'm not gonna ask you this question. But, Brian, I will ask you what? Why? You're different. I know you were first. He raced out. You got the press release out first. But now that you've been in the market for a while what up? Yours? Competitive differentiators. >> You know, there's there's really two out netted out for flash played on why we think it's a great fit for an A i N A. I use case. One is the flexibility of the performance. We call multi dimensional performance, small files, large files, meditated intensive workloads. Flash blade can do them all. It's a it's a ground up design. It's super flexible on performance. And but also more importantly, I would argue simplicity is a really hallmark of who we are. It's part of the modern date experience that we're talking about this morning. You can think about the systems. They are miniaturized supercomputers And yes, you could always build a supercomputer. People have been doing it for decades. Use Ph. D's to do it and, like most people, don't want to happen. People focused on that level of infrastructure, so we've tried to give incredible kind of capabilities in a really simple to consume platform. I joke with people. We have storage PhDs like literally people. Be cheese for storage so customers don't have to. >> Charlie, feel free to chime in on your favorite child if you want. I >> need a lot of it comes from our customers. That's how we first started with pure is our joint customers saying we need this stuff to work really fast. They're making a massive investment with us and compute. And so if you're gonna run those systems at 100% you need storage. The confusion, you know, pure is our first in there. There are longest partner in this space, and it's really our joint customers that put us together and, you know, to some extent, yes, we are Switzerland. You know, we love all of our partners, but, you know, we do incredible work with these guys all up and down the stack and that's the point to make it simple. If the customer has data we wanted to make be a simplest possible for them to run a ay, whether it's with my stuff with our cloud stuff, all of our partners, but having that deep level of integration and having some of the same shared beliefs to just make stuff simple so people can actually get value out of the data have I t get out of the way so Data scientists could just get their work done. That's what's really powerful about the partnership. >> And I imagine you know, we're out of time, but I imagine to be able to do this at the accelerated pace accelerated, I'm gonna say pun intended it wasn't but, um, cultural fed has to be pretty align. We know Piers culture is bold. Last question, Brian and we bring it home here. Talk to us about how the cultural cultures appearing and video are stars I lining to be able to enable how quickly you guys are developing together. >> Way mentioned the simplicity piece of it. The other piece that I think has been a really strong cultural fit between the companies. It's just the sheer desire to innovate and change the world to be a better place. You know, our hallmark. Our mission is to make the make the world a better place with data. And it really fits with the level of innovation that obviously the video does so like to Silicon Valley companies with wicked smart folks trying to make the world a better place, It's It's really been a good partnership. >> Echo that. That's just, you know, the rate of innovation in a I changes monthly. So if you're gonna be a good partner to your customers, you gotta change Justus fast. So our partnership has been great in that space. >> Awesome. Next time, we're out of time, But next time, come back, talk to a customer, really wanna understand it, gonna dig into some of the great things that they're extracting from you guys. So, Charlie Brian, thank you for joining David me on the Cube this afternoon. Thanks. Thanks. Thanks for David. Dante. I'm Lisa Martin. You're watching the Cube. Y'all from pure accelerate in Austin, Texas.

(lively music) >> Narrator: Live, from San Francisco, celebrating 10 years of high tech coverage, it's theCUBE, covering VMworld 2019! Brought to you by VMware and its ecosystem partners. >> Okay, welcome back to theCUBE's live coverage in VMworld 2019. We're in San Francisco. We're in Moscone North Lobby. I'm John Frer, my co Stu Miniman, here covering all the action of VMworld, two sets for theCUBE, our tenth year, Stu. Keeping it going. Two great guests, John Fanelli, CUBE Alumni, Vice President of Product, Virtual GPUs at NVIDIA Kevin Gray, Director of Product Marketing, Dell EMC. Thanks for coming back on. Good to see you. >> Awesome. >> Good to see you guys, too. >> NVIDIA, big news, we saw your CEO up on the keynote videoing in. Two big announcements. You got some stats on some Windows stats to talk about. Let's talk about the news first, get the news out of the way. >> Sure, at this show, NVIDIA announced our new product called NVIDIA Virtual Compute Server. So for the very first time anywhere, we're able to virtualize artificial intelligence, deep learning, machine learning, and data analytics. Of course, we did that in conjunction with our partner, VMware. This runs on top of vSphere and also in conjunction with our partner at Dell. All of this Virtual Compute Server runs on Dell VxRail, as well. >> What's the impact going to be for that? What does that mean for the customers? >> For customers, it's really going to be the on-ramp for Enterprise AI. A lot of customers, let's say they have a team of maybe eight data scientists are doing data analytics, if they want to move through GPU today, they have to buy eight GPUs. However, with our new solution, maybe they start with two GPUs and put four users on a GPU. Then as their models get bigger and their data gets bigger, they move to one user per GPU. Then ultimately, because we support multiple GPUs now as part of this, they move to a VM that has maybe four GPUs in it. We allow the enterprise to start to move on to AI and deep learning, in particular, machine learning for data analytics very easily. >> GPUs are in high demand. My son always wants the next NVIDIA, in part told me to get some GPUs from you when you came on. Ask the NVIDIA guy to get some for his gaming rig. Kidding aside, now in the enterprise, really important around some of the data crunching, this has really been a great use case. Talk about how that's changed, how people think about it, and how it's impacted traditional enterprise. >> From a data analytics perspective, the data scientists will ingest data, they'll run some machine learning on it, they'll create an inference model that they run to drive predictive business decisions. What we've done is we've GPU-accelerated the key libraries, the technologies, like PyTorch, XGBoost to use a GPU. The first announcement is about how they can now use Virtual Compute Server to do that. The second announcement is that workflow is, as I mentioned, they'll start small, and then they'll do bigger models, and eventually they want to train that scale. So what they want to do is they want to move to the cloud so they can have hundreds or thousands of GPUs. The second announcement is that NVIDIA and VMware are bringing Virtual Compute Server to VMware Cloud running on AWS with our T4 GPUs. So now I can scale virtually starting with fractional GPU to single GPU to multi GPU, and push a button with HCX and move it directly into AWS T4 accelerated cloud. >> That's the roadmap so you can get in, get the work done, scale up, that's the benefit of that. Availability, timing, when all of this is going to hit in-- >> So Virtual Compute Server is available on Friday, the 29th. We're looking at mid next year for the full suite of VMware Cloud on top of Aws T4. >> Kevin, you guys are supplier here at Dell EMC. What's the positioning there with you guys? >> We're working very closely with NVIDIA in general on all of their efforts around both AI as well as VDI too. We'll work quite a bit, most recently on the VDI front as well. We look to drive things like qualifying the devices. There's both VDI or analytics applications. >> Kevin, bring us up-to-date 'cause it's funny we were talking about this is our 10th year here at the show. I remember sitting across Howard Street here in 2010 and Dell, and HP, and IBM all claiming who had the lowest dollar per desktop as to what they were doing in VDI. It's a way different discussion here in 2019. >> Absolutely. Go ahead. >> One of the things that we've learned with NVIDIA is that it's really about the user experience. It's funny we're at a transition point now from Windows 7 to Windows 10. The last transition was Windows XP to Windows 7. What we did then is we took Windows 7, we tore everything out of it we possibly could, we made it look like XP, and we shoved it out. 10 years later, that doesn't work. Everyone's got their iPhones, their iOS devices, their Android devices. Microsoft's done a great job on Windows 10 being immersive. Now we're focused on user experience. When the VDI environment, as you move to Windows 10, you may not be aware of this, but from Windows 7 to Windows 10, it uses 50% more CPU, and you don't even get that great of a user experience. You pop a GPU in there, and you're good. Most of our customers together are working on a five-year life cycle. That means over the next five years, they're going to get 10 updates of Windows 10, and they're going to get like 60 updates of their Office applications. That means that they want to be future-proof now by putting the GPUs in to guarantee a great user experience. >> On the performance side too, obviously. In auto updates, this is the push notification world we live in. This has to built in from day one. >> Absolutely, and if you look at what Dell's doing, we really built this into both our VxRails and our VxBlocks. GPUs are just now part of it. We do these fully qualified. It stacks specifically for VDI environments as well. We're working a lot with the n-vector tools from VM which makes sure we're-- >> VDI finally made it! >> qualifying user experience. >> All these years. >> Yes, yes. In fact, we have this user experience tool called n-vector, which actually, without getting super technical for the audience, it allows you to look at the user experience based on frame-rate, latency, and image quality. We put this tool together, but Dell has really been taking a lead on testing it and promoting it to the users to really drive the cost-effectiveness. It still is about the dollar per desktop, but it's the dollar per dazzling desktop. (laughing) >> Kevin, I hear the frame-rate in there, and I've got all the remote workers, and you're saying how do I make sure that's not the gaming platform they're using because I know how important that is. >> Absolutely. There's a ton of customers that are out there that we're using. We look at folks like Guillevin as like the example of a company that's worked with us and NVIDIA to truly drive types of applications that are essential to VDI. These types of power workers doing applications like Autodesk, that user experience and that ability to support multiple users. If you look at Pat, he talked a little bit about any cloud, any application, any device. In VDI, that's really what it's about, allowing those workers to come together. >> I think the thing that the two of you mentioned, and Stu you pointed out brilliantly was that VDI is not just an IT thing anymore. It really is the expectation now that my rig, if I'm a gamer, or a young person, the younger kids, if you're under 25, if you don't have a kick-ass rig, (laughs) that's what they call it. Multiple monitors, that's the expectation, again, mobility. Work experience, workspace. >> Exactly, along those same lines, by the way. >> This is the whole category. It's not just like a VDI, this thing over here that used to be talked about as an IT thing. >> It's about the workflow. So it's how do I get my job done. We used to use words like "business worker" and "knowledge worker." It's just I'm a worker. Everybody today uses their phone that's mobile. They use their computer at home, they use their computer at work. They're all running with dual monitors. Dual monitors, sometimes dual 4K monitors. That really benefits as well from having a GPU. I know we're on TV so hopefully some of you guys are watching VDI on your GPU-accelerated. It's things like Skype, WebEX, Zoom, all the collaboration to 'em, Microsoft Teams, they all benefit from our joint solution, like the GPU. >> These new subsystems like GPUs become so critical. They're not just subsystem, they are the main part because the offload is now part of the new operating environment. >> We optimized together jointly using the n-vector tool. We optimized the server and operating environment, so that if you run into GPU, you can right-size your CPU in terms of cores, speed, etc., so that you get the best user experience at a most cost effective way. >> Also, the gaming world helps bring in the new kind of cool visualization. That's going to move into just the workflow of workers. You start to see this immersive experience, VR, ARs obviously around the corner. It's only going to get more complex, more needs for GPUs. >> Yes, in fact, we're seeing more, I think, requirements for AR and VR from business than we are actually for gaming. Don't you want to go into your auto showroom at your house and feel the fine Corinthian leather? >> We got to upgrade our CUBE game, get more GPU focused and get some tracing in there. >> Kevin, I know I've seen things from the Dell family on levering VR in the enterprise space. >> Oh, absolutely. If you look at a lot of the things that we're doing with some of the telcos around 5G. They're very interested in VR and AR. Those are areas that'll continue to use things like GPUs to help accelerate those types of applications. It really does come down to having that scalable infrastructure that's easy to manage and easy to operate. That's where I think the partnership with NVIDIA really comes together. >> Deep learning and all this stuff around data. Michael Dell always comes on theCUBE, talks about it. He sees data as the biggest opportunity and challenge. In whatever applications coming in, you got to be able to pound into that data. That's where AI's really shown... Machine learning has kind of shown that that's helping heavy lifting a lot of things that were either manual. >> Exactly. The one thing that's really great about data analytics that are GPU-accelerated is we can take a job that used to take days and bring it down to hours. Obviously, doing something faster is great, but if I take a job that used to take a week and I can do it in one day, that means I have four more days to do other things. It's almost like I'm hiring people for free because I get four more extra work days. The other thing that's really interesting as our joint solution is you can leverage that same virtual GPU technology. You can do VDI by day and at night, you run Compute. So when your users aren't at work, you migrate them off, you spin up your VMs that are doing your data analytics using our RAPIDS technology, and then you're able to get that platform running 24 by seven. >> Productivity gains just from an infrastructure. Even the user too, up and down, the productivity gains are significant. So I'll get three monitors now. I'm going to get one of those Alienware curved monitors. >> Just the difference we had, we have a suite here at the show, and just the difference, you can see such a difference when you insert the GPUs into the platform. It's just makes all the difference. >> John, I got to ask you a personal question. How many times have people asked you for a GPU? You must get that all the time? >> We do. I have a NVIDIA backpack. When I walk around, there's a lot of people that only know NVIDIA for games. So random people will always ask for that. >> I've got two sons and two daughters and they just nerd out on the GPUs. >> I think he's trying to get me to commit on camera on giving him a GPU. (laughing) I think I'm in trouble here. >> Yeah, they get the latest and greatest. Any new stuff, they're going to be happy to be the first on the block to get the GPU. It's certainly impacted on the infrastructure side, the components, the operating environment, Windows 10. Any other data you guys have to share that you think is notable around how all this is coming together working from user experience around Windows and VDI? >> I think one piece of data, again, going back to your first comment about cost per desktop. We're seeing a lot of migration to Windows 10. Customers are buying our joint solution from Dell which includes our hardware and software. They're buying that five-year life cycle, so we actually put a program in place to really drive down the cost. It's literally like $3 per month to have a GPU-accelerated virtual desktop. It's really great Value for the customers besides the great productivity. >> If you look at doing some of these workloads on premises, some of the costs can come down. We had a recent study around the VxBlock as an example. We showed that running GPUs and VDI can be up as much as 45% less on a VxBlock at scale. When you talk about the whole hybrid cloud, multi-cloud strategy, there's pluses and minuses to both. Certainly, if we look at some of the ability to start small and scale out, whether you're going HCI or you're going CI, I think there's a VDI solution there that can really drive the economics. >> The intense workloads. Is there any industries that are key for you guys in terms of verticals? >> Absolutely. So we're definitely looking at a lot of the CAD/CAM industries. We just did a certification on our platforms with Dassault's CATIA system. That's an area that we'll continue to explore as we move forward. >> I think in the workstation side of things, it's all the standard, it's automotive, it's manufacturing. Architecture is interesting. Architecture is one of those companies that has kind of an S and B profile. They have lots of offices, but they have enterprise requirements for all the hard work that they do. Then with VDI, we're very strong in financial services as well as healthcare. In fact, if you haven't seen, you should come by. We have a Bloomberg demo for financial services about the impact for traders. I have a virtualized GPU desktop. >> The speed is critical for them. Final question. Take-aways from the show this year, 2019 VMworld, Stu, we got 10 years to look back, but guys, take-aways from the show that you're going to take back from this week. >> I think there's still a lot of interest and enthusiasm. Surprisingly, there's still a lot of customers that haven't finished there migration to Windows 10 and they're coming to us saying, Oh my gosh, I only have until January, what can you do to help me? (laughing) >> Get some GPUs. Thoughts from the show. >> The multi-cloud world continues to evolve, the continued partnerships that emerge as part of this is just pretty amazing in how that's changing in things like virtual GPUs and accelerators. That experience that people have come to expect from the cloud is something, for me is a take-away. >> John Fanelli, NVIDIA, thanks for coming on. Congratulations on all the success. Kevin, Dell EMC, thanks for coming on. >> Thank you. >> Thanks for the insights. Here on theCUBE, Vmworld 2019. John Furrier, Stu Miniman, stay with us for more live coverage after this short break. (lively music)

>> live from Mountain View, California It's the queue covering definite create twenty nineteen. Brought to You by Cisco >> Welcome back to the Cube. Elisa Martin with Set Cisco Definite Create twenty nineteen at the Computer History Museum, but here all day, talking with some really great innovative folks excited to welcome to the Cube. Marty Jane, senior director of this Cisco Global Partnership and Video. Marty, It's great to have you here. >> Thank you. Good to be here. >> So I always love talking about partnerships Where what Day One of Dev. Net. Tomorrow's day to. There's been a lot of a lot of community spirit is here, so I just kind of in the spirit of partnerships, lot of collaboration that community is is really strong. Uh, before we get into kind of the details of this Cisco in video partnership first kind of thing, I wonder is all right. This is the developer community. Why the developer community within video? >> That's a great question. So if you think about way, make GP use, which is a piece of silicon graphics processing unit, and it is really only a piece of silicon until a developer comes along and develops a cool app on it. So if you think about how we go to market our large conferences called GTC, it's really developer. Focus. We have a little over a million developers in our ecosystem, and I find it very synergistic with Cisco. If you think about Suzy, we's vision. I think it's the same idea. You look at over half a million developers in their ecosystem and they want to develop collapse, and that's how your platform becomes relevant. So if you think of all the modern innovation that's coming from developers, so these are the folks that we should be talking to on a daily basis. I see a lot of commonality, a lot of synergies. In fact, we had Sisko definite come over to our conference GTC, and they they appeal to our developers. And now we're here talking to their developers and also developing some joint platforms which the the folks can use for. Like I said, the more modern *** with all the new data that's coming, whether the coyote with a machine learning automotive, smart cities, you name it, we need to be able to provide the platform to the developers >> and a number of those topics came up today, even during the keynote, Smart cities being able to utilize and accelerate work leads with a I and machine learning. They gave some great examples during the keynote of how developers can build networks. They give this cool example of I think it right off the hills of Coachella of designing a secure network for an indoor concert, designing it for an outdoor festival, Coachella and then designing it for a massive stadium like a big football game like the Super Bowl, for example. And they showed it that higher end. They showed how they're using machine, learning to zoom in on. For example, they had this little red box and you see people and what's actually in there than the machines detected was a fight and in real time, analysing this data and thence, dispatching the appropriate security to come and obviously probably take the drinks out of their hands first. But it was a really interesting, great real world example. So you guys have been partners a long time. Our you've been actually working at various companies with Cisco for a long time, but I think of Cisco and video coming together. How are you great? Something to accelerate these? Aye. Aye. And machine weren't were machine learning workloads that we're starting to see in every industry. >> You bet. Great question. So let me first comment on what you said about smart cities. I like to think of it as smart and safe cities. So actually, the first set of application will be around public safety. What the example you were giving his spot on? If you have large crowds gathering, it makes sense for us to be able to look at those clouds. Crowds? We call it intelligent video analytics or idea. In fact, we have a platform here. The Sisko i R eleven o one with a GPU added to it. So now I can wash the crowds. And if there's a fight breaking out or somebody's carrying in a weapon, you want to know somebody walks in carrying a backpack and drops it and moves on. You want to know one? Inform somebody. So what is happening is way of these millions and millions of bites of video data, >> and >> that data is not being really used today. So what we're doing is saying you know what? Let's find those pieces of intelligence and the video data and do something with it. And public safety is absolutely the highest priority. So smartest, safe city makes a lot of sense. So what we're doing is we're going to market with partners at Cisco. So what we're doing is we're saying Okay, let's design these GPS into the servers, which are connected to cameras and think about how many cameras are deployed today, probably a billion. And a lot of the video data can now be used for public safety purposes, and we basically go out and talk to large companies. We talked to governments. We talked to cities along with Sisko to go even open their eyes to what is possible today. >> Right? Because of that data is dark for so long, they don't know what they don't know. >> While most cases, what happens is you record four days of video and until something happens, nobody goes back and takes a look at it. But now we have the ability to look at the real time and cities and government's desire that very much so, >> sir example, that's such a relevant topic. I mean, they know. There's also the issue of privacy. But to your point about not just a smart city but a smart, safe city. I like that. I think it's absolutely imperative. How do you have this conversations with cities with governments about All right, this is what we want. Do we want to actually apply machine learning? So the machines are taught What that line is with privacy with those boundaries are so that a person, I'd say a lay person not in technology. Maybe is a city government official who doesn't understand the technology or need Teo will go. I get it. >> Yes. So our conversations are really about what we call you cases. So think of enterprise. A good use case would be. In fact, we work with Cisco on developing use case. You know, you always badge in into an enterprise. You have your badge, you walk in. But you also have some cases. People follow you, following you in what stops you from following me into a building. And usually people are too polite to say no, you can walk in, but we've >> all had the video training or read the manual. We know we're not >> we're not supposed to bite, but >> then you're like, I >> don't just cultural, exactly. We just can't you know that. So now we have the ability. So we trained a in a network to say, Look, if Marty's badging in, only he's allowed to walk in. And if there's a second person walks in, I want to take put Little Red Square on that face and inform security that we have had more than one person walking. So these are some of the ways. So we talk about use cases. This is one use case crowd behavior. Analytics is another use case. You know, people were walking in the backpack, dropping it. Other use case would be something like Bar to Bart loses millions of dollars year because people jumped the turnstiles and Bart didn't really have a good way of of monitoring, measuring the losses until we put a camera and captured the number of people that were jumping. The turnstiles are going in through the handicap access, okay? They were losing ten times the dollar value of what we had thought. Wow. So this is how we start the conversation with use cases, you know? And what would you like to do? Being able to count the number of cars in intersection begin with counter number of pedestrians, so you could do traffic management better. That's the language we would use with cities and governments. And then we go deeper as you go through the implementation process. >> Well, that makes perfect sense going in the use case route, because you can clearly see in that example that you mentioned with Bart a massive business outcome and an opportunity to regain a tremendous amount of resource is that they could redeploy for whether it it's new trains, new trucks, etcetera than them, not realizing we're losing how much money. I think anybody when you could put the useless in that context of this is what you can expect as an outcome. They get it >> Absolutely. That's the really the only way to start the conversation than starting from bits and bytes. And this is the This is usually the case across industries. If you think about retail, for example, you know you go to a safe way to start talking about GPS and servers. That's not the great way to start, but they do have issues with shoplifting, for example. So how do you know a person is walking in, you know, through the checkout. And they have one item. Then there's a small item right here and they walk out with this. How do you monitor that? So now you can do that with the right kind of cameras that can capture. Look there Two items, not one. How do you know where shop are stopping Which aisle is the most popular? I'Ll How do you know that? Well, now you can have cameras would say, Look, we have red zones and Green Zone so you could do those kinds of things with modern ways of doing. I >> so interesting because it's so. I mean, the examples that you gave are so disparate, but yet they make so much sense was how how you're describing it rather than going into, you know, a grocery store in talking about GPS, which they might fall over with their eyes. Doing this >> right. >> You're actually putting in the context of a real world problem they've been experiencing since the beginning of time. Don't you understand? Only goodness and this is how we can use technology. It's the safe way becomes a technology company. They don't know it. What actually started packing their bottom line. >> That's right, And so even now, you know. So I have to take that and you extend that into How do you go to market? And it's something you wanted Teo Touch on. How do you go to market with Cisco's? How does ingredients is? Could do it together, right? So think of Cisco's sales teams who are talking to all these customers every day where their retailers, financial services, federal government, health care, you name it. So what we've done is we basically sort of taking all these industries and created the top three or four use cases we know are relevant to that industry, either for safety or for saving money's. For variety of their operational reason, we have narrowed it down to three or four five use cases and each of those target industries. So what we do now with Cisco teams that we would bring them into our facility or go to them and really talkto all those use cases and train them on Hey, look, this is what we do jointly, and that makes the conversation much easier. Then they will go and present to the customer and what's the customer gets an idea far this all possible. Now that starts a deeper level technology and server and GPU engagement. So this is one way we go up and talk to different customers. What's the school's >> second? About a bit. Marcus. Cisco is so enormous, they have a billion different. I'm slightly exaggerating products with but a lot of different technologies that form many different solutions. So I imagine your Cisco expertise over many years of working with Cisco's a partner for other companies. How do you once you get to that deeper level conversation, how do you bring this different groups within Cisco together? So that that solution conversation is one that really aligns to that use case and the customer doesn't get it? >> Yeah, that's a difficult question to answer. That's like, you know your work. It's just cause a large company. But I think I also think they're also very cells driven, and that's what drives the different groups to come together. In fact, some people called me the Connector because I've been working. Cisco's so long. I know people and definite I know people in sales. I know people in the server. BU, in fact, if you think about the The platform was talking about the i r eleven o one with the jets and GPU that came as a result. I was talking to the i o t bu result talking to Dev net our situation the definite he said. You know what? This is cool are gonna do this. Then we take that to the IOC Guys is Oh, this is cool. We can take that. Put it in this platform, and then I'm next. Actually, next week I'm talking to a sale. Seaman Cisco. They cover utilities. And this platform was profit for utilities. Even think about fire monitoring in a forest. How do you do, boy thousand? The people to just watch what happens. We can take a platform like that now and really deploy it in hundreds of places which could monitor fires or the starting off a fire. But yes, bringing them together. It is no easy task. It's fun >> where you are smiling. I like that. Marty the connector. Jane, thank you >> so much for >> joining me on the kid this afternoon. Fun conversation. I enjoyed it. >> Ofcourse. Thank you. Likewise. Thank >> you, Lisa Martin for the Cube. you're watching us live, Francisco Definite. Create twenty nineteen. This is the end of day one. Stick around, John. Failure on I will be back tomorrow to cover day too. Thanks for watching.

>> Announcer: Live from Las Vegas, it's theCUBE, covering NetApp Insight 2018. Brought to you by NetApp. >> Welcome back to theCUBE, we are live. We've been here all day at NetApp Insight in Las Vegas at the Mandalay Bay. I'm Lisa Martin with Stu Miniman and we're joined by a couple of guests. One of our alumni, Bharat Badrinath, the V.P. of Product Solutions and Marketing at NetApp. Hey, Bharat, welcome back. >> Thank you, thanks for having me. >> And we've also got Renee Yao, who is a Senior Product Marketing Manager for Deep Learning and AI Systems at Nvidia. Renee, welcome to theCUBE. >> Thanks for having me. >> So guys, this is a pretty big event. NetApp's biggest customer-partner event, the keynote, standing room only this morning five thousand plus people, lot of buzz, lot of momentum. Speaking of momentum, NetApp and Nvidia just launched an interesting partnership a couple months ago. Bharat, talk to us about how NetApp is working with Nvidia to really take advantage of AI and allow your customers to do that as well. >> Sure. So, as we started talking to customers and started looking at what they were investing in, AI bubbled up, right up to the top. And given our rich history in NFS, high performance NFS, it became an obvious choice for NetApp to invest in this space. So we've been working with Nvidia for a really long time, probably close to a year, to start integrating our products with their DGX-1 supercomputer and providing it as a single package to our customers, which makes it a lot easier for them to deploy their AI instead of waiting months for testing infrastructure, which the data scientists don't want to do. We get them a pre-tested, pre-validated system and our All-Flash Fast, which has been winning multiple awards and the recent A800 announcement were perfect choice for us to integrate into this architecture for the system. >> Alright, Renee, in the keynote this morning, the Futurist, he said-- We talked about data as the new oil, he said AI is the new electricity. Maybe you can speak a little bit as to why this is so important. Having gone to a lot of shows this year, it felt like every single show I go to, I see Nvidia, arm in arm with partners, because there's a huge wave coming. >> Yes, absolutely, and I think there was this hype about data, there was this hype about AI, and I think the years of Big Data World, that's creating data, absolutely the foundation for AI, and AI as the new electricity is a very, very good analogy. And let's do some math, shall we? So Swiss Federal Railway, it's a very good customer of ours. For those of you who don't know, they're kind of like the heart or center of all the railway tracks going through, serving about 1.2 million passengers on a day-to-day basis. Securing their security is very, very important. Now, they also have a lot of switches that turn on, then the train can go by and with the tunnels and bridges and switches, so they need to make sure that these trains actually don't collide. So when one train goes by with 11 switches, that gives you 30 ways of possible routing. Two trains, 900 ways. 80 trains, 10 to the eightieth power of ways. That's more than the observed atoms in the universe. And they actually have more than 10 thousand trains. So think about, can human being possibly calculate that much data and possibilities in their brain? As smart as we all want to think we all are, they turn to DGX, and the full day of simulation on DGX-1 was only 17 seconds for them to get back results. And I think that analogy of AI as the new electricity, just talking about the speed of light, is very spot on. >> So this isn't hype anymore, this is actually reality. And you gave a really great example of how a large transportation system is using it to get almost real time information. Bharat, talk to us about NetApp storage, history, 26 years, you guys have really made a lot of pivots in terms of your digital transformation, your cultural transformation. How are you helping with, now, kind of the added power of Nvidia, helping customers to, the hype's gone, actually deploy it, live it, and benefit a business from it? >> Yeah, absolutely, I think, as you rightly pointed out, NetApp has made a lot of pivots. Right, and I think the latest journey in terms of being empowering our customers with data has been a very powerful mission for the company. We entered the Flash market a little bit later than our competitors, but we have made dramatic progress in that space. In fact, recently, based on the latest IDC report, we were number one in All-Flash market worldwide, so that is quite an accomplishment for a company which was late to the market. And having said that, that's because of the innovation engine that is still alive and well within NetApp. We're announcing, as you've seen in the conference, we're announcing a lot of new products and technology which are way ahead of what our competitors are offering, but I think it is all hinged on what our customers need. The customer benefits because, yeah, it has profound benefit of changing how customers operate, their entire operations, it can transform dramatically overnight. And as Renee pointed out, Big Data gave the foundation which collected all the data, but wasn't able to process it. But AI with the power of Nvidia and DGX is able to utilize that to create those outcomes for customers. And from our perspective, we bring two key value adds to the space. One, we're able to serve up the data at incredibly high speeds with our award-winning All-Flash systems. But more importantly, data today lives everywhere. If you think about it, edge is becoming even more important. You can't expect an autonomous car to make an instantaneous decision without the backing of data, which means it can't, everything can't reside in the cloud, it may be at the edge. Some of it may be at your data center. How do you tie all three together, edge, core, and cloud? And that's where the data fabric, the vision of data fabric that you saw today comes in the picture. So one is performance, the ability to stream up the kind of data at the speed of the new processors are demanding, at the speed the customers are demanding to make business decisions and also the edge to core to cloud, our data fabric, which is unique and unparalleled in the industry. >> Now, I'm wondering if you could both bring us inside the customers a little bit. If I think of the traditional storage customer, I need performance, I have more and more data that I need to deal with. But Renee pointed out real outcomes, which is beyond what a traditional storage person would be doing. Who are you working with at the customers-- How do they put together-- It almost sounds like you're building a car. I've got the engine, I've got all the pieces. Who helps put this whole solution together? How does the partnership on the customer's side go together? >> That's a great question. I'll give my take and you can jump on it because she's just returned from being on road shows with joint customers and prospects. So I believe it has to be a joint decision. It's not like IT does it first and the data scientists come in later. Although it may be the case in certain instances where the data scientists start the discussion and then the IT gets brought in. In an ideal case, just like building a car, you want all the teams to be sitting together, make sure they're making the right calls because every compromise you make at one end will impact the other. So you want to make sure you make the optimal decision end to end. And that's where some of our channel partners come in who kind of bridge the data scientist team and the IT team. In some cases, customers show up with data scientists and IT teams together and some, it's one after the other. >> Absolutely. We see the same thing when we're on the road show. Literally two weeks ago, in Canada, by the way, there was a snowstorm, and it was an unforeseen snowstorm, you don't get snowstorm in October-- >> Yes, even for Canada, it was unforeseen. >> Yeah, and we had a packed room of people coming to learn about AI and in the audience, we absolutely see people from the infrastructure side, from the data center side, from the data scientist side, and they realized that they really have to start talking because none of them can afford to be reactive. For example, the data scientists, we want to do the innovation. I can't just go to the infrastructure guys and say that, "Hey, this is my workload, do something about it." And the infrastructure guys don't want to hold on to that problem and then don't know what to do with it. They really need to be ahead of everything and I think the interesting thing is, among those four cities that we're at, we see customers from the government, oil and gas, transportation, health care, and just any industry you can think of, they're all here. One specific example, do you know Mike's company that actually came to us, they have about 15 petabytes of data and that's storing 20 years of historical data and they only have two staff and they were not hiring more staff. They were like, "We just want something that's "going to be able to work and we know everything, "so just give us a solution that's going to be able to "easily scale up and out and enable us to continue to "store more data, manage more data, "and get insights out of data fast." So they came to both of us, it's just a very good, natural decision. That's why we have a partnership together as well. >> So you guys talked about kind of connecting the data scientists with the infrastructure folks. Where's the business involved in this conversation? In terms of, we want to identify new products and services to deliver faster than our competition, new markets. Talk to us about, are the data scientists and the infrastructure guys and girls following business initiatives that have been set or are the business leaders involved in these joint conversations? >> Go ahead, you take it. >> Sure. So, I think we see both. We definitely see that there's top-level executives saying that this is our initiative and we have to do it. And they will make the decision that we have to refresh our infrastructure from the ground up to make sure we're supportive of our data scientists' innovation. We've also seen brilliant minds, researchers, data scientists doing amazing things and then roll it up to the VP level and then roll it up to CEO level to say that this has to be done because this-- For example, that simulation of 17 second results, it's things that people used to cannot do in their lifetime, now they can do it in seconds, that kind of innovation just cannot be ignored. >> Yeah, we see the same thing. In fact, any team that has possession of that data or is accountable for that data is the one usually driving the decisions. Because as you mine the data, as you start deploying new techniques, you realize new opportunities, which means the business gets more interested in it and vice versa. If the business is interested, they're going to look for those answers within the data that they have. >> So last thing, Renee, you were on the Women in Tech panel that ended yesterday, Bharat and I were both in the audience, and one of the things that I thought was really inspiring about your story is that you had given us, the audience, an interesting example of a TV opportunity that you were inspired to do by the CEO of Nvidia. Give our audience who didn't have a chance to see that panel a little bit, and in the last minute, of that story and how you were able to step forward and go, "I'm going to try this." >> Yeah, of course. I think that brings us back to the concept that we have at Nvidia, the speed of light concept, and you really have to learn, act, to move at the speed of light, just like our GPUs, with extreme performance. And obviously, at that speed, none of us know everything. So what Jensen, CEO, shared with us was, in an all-hands meeting internally, he told us that none of us are here qualified to do any of our jobs, maybe besides his legal counsel and CFO. And all of us are here to learn, and we need to learn as fast and as much as we can. And we can't really just let the competition determine where our limit is, but instead is by the limit of what is possible. So that is very much a fundamental mindset change in this AI revolution. >> Well thanks so much, Renee and Bharat, for stopping by and sharing with us the exciting things that you guys are doing with NetApp. We look forward to talking with you again soon. >> Thank you. >> Me too, thanks. >> For Stu Miniman, I'm Lisa Martin. You're watching theCUBE, live from NetApp Insight 2018 in Las Vegas. Stu and I will be right back with our next guests after a short break. (techno music)

>> Live from San Francisco, it's theCUBE, covering Micron Insight 2018. Brought to you by Micron. >> Welcome back to San Francisco everybody. You're watching theCUBE the leader in live tech coverage. I'm Dave Vellante. He's David Floyer, and we're covering Micro Insight'18. It's all about bringing together artificial intelligence and the memory and storage requirements. We're here on the embarcadero. We've got treasure island that way. We've got the financial district over there. We've got Golden Gate bridge behind us. Tom Eby is here as senior vice president and GM of Micron's booming compute and networking business unit. Good to see you Tom. >> Great to be here. >> And Permal Savla is here. He's the director of deep learning at NVIDIA. Welcome. >> Thank you. >> So obviously some of these new emerging work loads require collaboration between folks like Micron and folks like NVIDIA. But Tom why don't you kick it off. What are some of the big trends that you're seeing in some of these alternative work loads that's driving this collaboration? >> Well a lot of what we're talking about here today is the drive of AI and machine learning work loads, and the implications for memory. Certainly there's a host of them, natural language processing, photo and image recognition, applications in medical research, applications in optimizing manufacturing like we're doing in our fabs, and there's many many more. And of course what's exciting for us is that to support those in an optimized way really does require the mating of the optimal processing architecture, things like GPUs. With the right high band width with low latency memory and storage solutions. That's what leads to great partner ships between partnerships like Micron and NVIDIA. >> David was explaining at our open the intensity of the work loads that you guys are serving, and how much more resources that requires to actually deliver the type of performance. Maybe you could talk about some of the things that you're seeing in terms of these emerging work loads. >> Yes, so at NVIDIA, we build systems for X rated computing. AI and deep learning is a very quickly expanding field at this point which needs a lot of CP horse power. What we are seeing is that different applications like you said there's image processing, whether it's video, whether it's natural language processing the amount of data that is there, that is required to do deep learning and AI around it, we break it up into two work flows. One is the training where you actually train the software, and make it intelligent enough to then go and do inference later on. So that you can go and get you results out of it at the end of it. We concentrate on this entire workflow. That's where when we are looking at it from a training perspective, the GPU gives it the processing power. But at the same time all the other components around it perform at the peak. That's where the memory comes in. That's where the storage comes in, and we need to process that data very quickly. >> Yeah, so we know from system's design that you got to have a balanced system or else you're just going to push the bottle necks around. We've learned that over the years, but so it's more than just slapping on a bunch of storage and a bunch of memory. You're doing some other deeper integration, is that correct and what is that integration? >> Yeah, I think the two companies have had a great relationship, just to talk about a couple examples. We essentially co-defined a technology called GEDR 5X, which greatly enhanced the speed of graphics technology. We gently introduced that to the marketplace with NVIDIA about 18 months ago. And then worked with them again very closely on a technology called GDDR six, which is the next generation of even faster technology. We were their launch and ran partner for their recently announced G-force RTX line of cards. It's a very deeply engaged early in the process, define the process, define the standards, jointly develop the solution. Very intimate sharing in the supply chain area. It's a great relationship for us. We're excited about how we can continue to expand and extend that relationship by going forward. >> So obviously there's the two parts of it. You said the learning part of it, and the inference part of the computing. What do you think is the difference between the two? I mean obviously at the end of the day, the inference part is critical. That's got to be the fastest response time. You have to have that in real time. Can you talk a little bit about what you're doing to really speed that up, to make that micro seconds as opposed to milliseconds? >> So from an NVIDIA perspective we build the entire end to end tools steps for training and inferencing. We have a set of libraries that we have made it openly available for all of our customers, all our partners, and all users. So that they can go download it, and do the training so they can use the different frameworks and libraries to accelerate the work that they're doing. And then transform it onto the inference spot. We have something called denser RT, which is basically denser real time. That gives the capability to get these answers very quickly. So on our D4 of the tuning, Chip said that we just announced. We can get a very high performance for our image. So any kind of image recognition or image processing that we need to do, we can do that on the systems very quickly. And we can meet, rebuild entire architectures. So it's not just about one piece. It's about the whole end to end architecture of the system. >> So we heard earlier today in the analyst briefing, the press briefing that Micron certainly in the last 40 years has changed. We're seeing a lot more diversity. Usually it'd be all about PCs. Now there's just so many alternative work loads emerging. Clearly NVIDIA is playing there as well with alternative processing capabilities. What do you guys see as some of the more exciting, emerging work loads that are going to require continued collaboration and innovation? >> Yeah, well I think to build a little bit on some of the other comments about the need for real time inference, one of the things in the area of diversity that we've found interesting. The relationship between Micron and NVIDIA in high performance memory really started around their graphics business. But we are seeing in other markets closer to the edge, in automotive, in networking and in other areas where there's a need for that real time performance. Yet there's also a need for a degree of cost effectiveness. Perhaps a little more so than in the data center. That we're seeing technologies like GDR six being applied to a much broader range of applications like automotive, like networking, like Edge AI, to provide the performance to get that real time response but in a form factor and at a cost point that's affordable for the application. >> Anything you'd add to that Permal? >> So I would also add you talked about applications, different applications that are changing right? Today we announced a new set of libraries and tools for the analytic space. That's again a big work load in the enterprise data centers, that we are trying to optimize and accelerate with machine learning. So we announced a whole set of tools which take in these large data sets that are coming in, and applying it in the data centers and using it to get answers very quickly. So that's what NVIDIA is also doing is expanding on these capabilities as we go in. And as these components and as these technologies get better it just gets our answers much more quickly. >> As exacts in the space and you guys both, you're component manufacturers, and so you sell to people who sell to end consumers. How do you get your information in that sort of pull through? Obviously you work with your customers very closely. >> Mm-hm. >> How do you get visibility to their customers? Just going to go to shows, you go do joint sales calls, how does that all work? >> Certainly some of that is in discussions with our customers and their marketing groups about what they're seeing from a customer point of view. But certainly there's other paths. One of the reasons behind the hundred million dollar venture fund that we announced today, is one of the best ways to get that advanced insight, is to be working with some of the most innovative start ups that understand what some of those end users needs might be and are developing some unique technologies. So there's a range. Working with our customers through eventually finding others, but it's important that we understand those needs because the lead time to developing the solutions both memory and processing architectures is quite well. >> Of course everybody wants to work with NVIDIA, you guys have an inundated like come on oh no we're the most. We're tied up now. Of course there's not a lot of choices here when you're talking about the levels of components that you're selling. But what's life like at NVIDIA? I mean they've been knocking down your doors to do partnerships. >> I think we've grown from being just the component to now being a complete system and an architecture. We don't only just build just a chip that the GPU was. We also build full SLCs. We also build the libraries, software, and the tools that are required to make this complete end to end solutions. We also do a lot of open source technologies because we want our customers and our end cast partners to build and take what we have and go beyond what it's capable of. That's where we end value at the end of the day. Yes, it's all of us together. We need to work together to make that much more faster as we go. >> The tuning is incredibly important. This is complicated stuff. It doesn't just work out of the box, right? So you need an ecosystem as well. >> Yes. >> Yes. >> That's what you guys have been out building. Tom, well give your final thoughts. >> Yeah well I guess to build a little bit. Certainly NVIDIA is moving up the stack in terms of the ecosystem, the software, the complete solution and I think Micron does as well. Like you commented, traditionally it was a component play. And increasingly, we're going to be building subsystems in memory and storage that occurs today on the storage side. I think we'll increasingly see that in memory, and with some of the future, very promising technologies like 30 Cross Point. >> Yeah it's the dawn of the days where everybody just gets piece parts and put them all together. They need you you guys to do more integration, and more out of the box like you say subsystems. So guys thanks very much for coming on theCUBE. Really appreciate it. >> Thank you. >> Thank you. >> Alright you're welcome, keep it right there everybody. We'll be back in San Francisco, you're watching theCUBE from Micron Insight 2018, accelerate intelligence. We'll be right back after this short break. (music)

[Music] [Applause] I'll Buena Burris and welcome to another cube conversation from our fantastic studios in beautiful palo alto california today we're going to be talking about what infrastructure can do to accelerate AI and specifically we're gonna use a relationship a burgeoning relationship between PDN and nvidia to describe what we can do to accelerate AI workloads by using higher performance smarter and more focused of infrastructure for computing now to have this conversation we've got two great guests here we've got Kurt ku kind who is the senior director of marketing at ddn and also Darren Johnson is a global director of technical marketing for enterprise and NVIDIA Kurt Gerron welcome to the cube thanks for thank you very much so let's get going on this because this is a very very important topic and I think it all starts with this notion of that there is a relationship that you guys have put forward Kurt once you describe it sure well so what we're announcing today is ddn's a3i architecture powered by Nvidia so it is a full rack level solution a reference architecture that's been fully integrated and fully tested to deliver an AI infrastructure very simply very completely so if we think about how this is gonna or why this is important AI workloads clearly have a special stress on underlying technology Darin talk to us a little bit about the nature of these workloads and why in particular things like GPUs and other technologies are so important to make them go fast absolutely and as you probably know AI is all about the data whether you're doing medical imaging whether you're doing natural language processing whatever it is it's all driven by the data the more data that you have the better results that you get but to drive that data into the GPUs you need great IO and that's why we're here today to talk about ddn and the partnership of how to bring that I owe to the GPUs on our dgx platforms so if we think about what you described a lot of small files off and randomly just riveted with nonetheless very high-profile jobs that just can't stop midstream and start over absolutely and if you think about the history of high-performance computing which is very similar to a I really I owe is just that lots of files you have to get it they're low latency high throughput and that's why ddn's probably nearly twenty years of experience working in that exact same domain is perfect because you get the parallel file system which gives you that throughput gives you that low latency just helps drive the GPU so we you'd mention HPC from 20 years of experience now it used to be that HPC you'd have scientists with a bunch of graduate students setting up some of these big honkin machines but now we're moving into the commercial domain you don't have graduate students running around you don't have very low cost high quality people you're you know a lot of administrators who nonetheless good people but a lot to learn so how does this relationship actually start making or bringing AI within reach of the commercial world exactly where this reference architecture comes in right so a customer doesn't need to start from scratch they have a design now that allows them to quickly implement AI it's something that's really easily deployable we've fully integrated this solution ddn has made changes to our parallel file system appliance to integrate directly within the DG x1 environment makes that even easier to deploy from there and extract the maximum performance out of this without having to run around and tune a bunch of knobs change a bunch of settings it's really gonna work out of the box and the you know nvidia has done more than just the DG x1 it's more than hardware you've done a lot of optimization of different of AI toolkits if Sarah I'm talking what about that Darin yeah so I mean talking about the example I use researchers in the past with HPC what we have today are data scientists data scientists understand pie tours they understand tensorflow they understand the frameworks they don't want to understand the underlying filesystem networking RDMA InfiniBand any of that they just want to be able to come in run their tensorflow get the data get the results and just turn that keep turning that whether it's a single GPU or 90 Jex's or as many dejection as you want so this solution helps bring that to customers much easier so those data scientists don't have to be system administrators so a reference architecture that makes things easier but that's more than just for some of these commercial things it's also the overall ecosystem new application providers application developers how is this going to impact the aggregate ecosystem it's growing up around the need to do AI related outcomes well I think one point that Darrin was getting to you there and one of the big effects is also as these ecosystems reach a point where they're going to need to scale right there's somewhere where ddn has tons of experience right so many customers are starting off with smaller data sets they still need the performance a parallel file system in that case is going to deliver that performance but then also as they grow right going from one GPU to 90 G X's is going to be an incredible amount of both performance scalability that they're going to need from their i/o as well as probably capacity scalability and that's another thing that we've made easy with a3i is being able to scale that environment seamlessly within a single namespace so that people don't have to deal with a lot of again tuning and turning of knobs to make this stuff work really well and drive those outcomes that they need as they're successful right so in the end it is the application that's most important to both of us right it's it's not the infrastructure it's making the discoveries faster it's processing information out in the field faster it's doing analysis of the MRI faster it's you know helping the doctors helping the anybody who's using this to really make faster decisions better decisions exactly and just to add to that I mean in automotive industry you have datasets that are from 50 to 500 petabytes and you need access to all that data all the time because you're constantly training and Retraining to create better models to create better autonomous vehicles and you need you need the performance to do that ddn helps bring that to bear and with this reference architecture simplifies it so you get the value add of nvidia gpus plus its ecosystem of software plus DD on its match made in heaven Darren Johnson Nvidia Curt Koo Kien ddn thanks very much for being on the cube thank you very much and I'm Peter burrs and once again I'd like to thank you for watching this cube conversation until next time [Music]

(microphone not on) >> be 47 (laughter) >> Are you ready? >> Here we go, alright and, three, two... >> You know it's great to see real benchmarking data, because this is a very important domain and there is not a lot of benchmarking information out there around some of these other products that are available. But let's try to to turn that benchmarking information into business outcomes, and to do that we got, Kurt Kuckein, back from DDN. Kurt welcome back let's talk a bit about how are these high value outcomes that business seeks with AI going to be achieved as a consequence of this new performance, faster capabilities, etcetera. >> So there's a couple of considerations, the first consideration I think is just the selection of AI infrastructure itself. Right, we have customers telling us constantly that they don't know where to start. Now that they have readily available reference architectures that tell them, hey here's something you can implement get installed quickly, you're up and running, running your AI from day one. >> So the decision process for what to get is reduced. >> Exactly. >> Okay. >> Uh, number two is you're unlocking all ends of the investment with something like this right? You're maximizing the performance on the GPU side. You're maximizing the performance on the ingest side for the storage. You're maximizing the through-put of the entire system, so you're really gaining the most out of your investment there. And not just gaining the most out of the investment, but truly accelerating the application and that's the end goal right, that we're looking for with customers. Plenty of people can deliver fast storage, but it does- If it doesn't impact the application and deliver faster results, cut run times down, then what are you really gaining from having fast storage? And so that where we're focused, we're focused on application acceleration. >> So simpler architecture, faster implementation based on that, integrated capabilities, ultimately, all revealing or all resulting in, better application performance. >> Better application performance, and in the end something that's more reliable as well. >> Kurt, thanks for again for being on The Cube. >> Thanks for having me.

(laughing) >> This is V-3. >> Alec, you're going to open up, we're going to cut, come to you in a second. Good luck, buddy. Okay, here we go. Alright Peter, ready? >> Yup. >> And we're coming to you in. >> Hold on guys, sorry, I lied. (laughing) V-2, V-3, there it is. Okay, ready. >> Now you're ready? >> Yup. >> You're ready ready? Okay here we go, ready and, three, two. >> Hi, I'm Peter Burris, welcome to another Cube Conversation from our wonderful studios in beautiful Palo Alto, California. Great conversation today, we're going to be talking about the relationship between AI, business, and especially some of the new infrastructure technologies in the storage part of the stack. And to join me in this endeavor is Kurt Kuckein, who's a senior director of product marketing at DDN. Kurt Kuckein, welcome to The Cube. >> Thanks, Peter, happy to be here. >> So tell us a little bit about DDN to start. >> So DDN is a storage company that's been around for 20 years. We've got a legacy in high-performance computing, and that's what we see a lot of similarities with this new AI workload. DDN is well-known in that HPC community; if you look at the top 100 supercomputers in the world we're attached to 75-percent of them and so we have a fundamental understanding of that type of scalable need that's where we're focused, we're focused on performance requirements, we're focused on scalability requirements, which can mean multiple things, right, it can mean the scaling of performance, it can mean the scaling of capacity, and we're very flexible. >> Well let me stop you and say, so you've got a lot of customers in the high-performance world, and a lot of those customers are at the vanguard of moving to some of these new AI workloads. What are customers saying? With this significant engagement that you have with the best and the brightest out there, what are they saying about this transition to AI? >> Well I think it's fascinating that we kind of have a bifurcated customer base here, where we have those traditionalists who probably have been looking at AI for over 40 years, right, and they've been exploring this idea and they've gone through the peaks and troughs in the promise of AI, and then contraction because CPUs weren't powerful enough. Now we've got this emergence of GPUs in the supercomputing world, and if you look at how the supercomputing world has expanded in the last few years, it is through investment in GPUs. And then we've got an entirely different segment, which is a much more commercial segment, and they're maybe newly invested in this AI arena, right, they don't have the legacy of 30, 40 years of research behind them, and they are trying to figure out exactly, you know, what do I do here? A lot of companies are coming to us, hey, I have an AI initiative, well what's behind it? Well, we don't know yet, but we've got to have something and they don't understand where is this infrastructure going to come from. >> So the general availability of AI technologies, and obviously Flash has been a big part of that, very high-speed networks within data centers, virtualization certainly helps as well, now opens up the possibility for using these algorithms, some of which have been around for a long time, but have required very specialized bespoke configurations of hardware, to the enterprise. That still begs the question, there are some differences between high-performance computing workloads and AI workloads. Let's start with some of the, what are the similarities, and then let's explore some of the differences. >> So the biggest similarity, I think, is just it's an intractable, hard IO problem, right, at least from the storage perspective. It requires a lot of high throughput, depending on where those IO characteristics are from, it can be very small-file, high-op-intensive type workflows, but it needs the ability of the entire infrastructure to deliver all of that seamlessly from end to end. >> So really high-performance throughput so that you can get to the data you need and keep this computing element saturated. >> Keeping the GPU saturated is really the key, that's where the huge investment is. >> So how do AI and HPC workloads differ? >> So how they're fundamentally different is often AI workloads operate on a smaller scale in terms of the amount of capacity, at least today's AI workloads. As soon as a project encounters success, what our forecast is, is those things will take off and you'll want to apply those algorithms bigger and bigger data sets. But today, you know, we encounter things like 10-terabyte data sets, 50-terabyte data sets and a lot of customers are focused only on that. But what happens when you're successful, how do you scale your current infrastructure to petabytes and multi-petabytes when you'll need it in the future? >> So when I think of HPC, I think of often very, very big batch jobs, very, very large, complex data sets. When I think about AI, like image processing or voice processing, whatever else it might be, I think of a lot of small files, randomly accessed. >> Right. >> That require nonetheless some very complex processing, that you don't want to have to restart all the time. >> Right. >> And a degree of simplicity that's required to make sure that you have the people that can do it. Have I got that right? >> You've got it right. Now one, I think, misconception is, is on the HPC side, right, that whole random small file thing has come in in the last five, 10 years and it's something DDN's been working on quite a bit, right. Our legacy was in high-performance throughput workloads, but the workloads have evolved so much on the HPC side as well, and, as you posited at the beginning, so much of it has become AI and deep-learning research >> Right, so they look a lot more alike. >> They do look a lot more alike. >> So if we think about the revolving relationship now between some of these new data-first workloads, AI-oriented, change the way the business operates types of stuff, what do you anticipate is going to be the future of the relationship between AI and storage? >> Well, what we foresee really is that the explosion in AI needs and AI capabilities is going to mimic what we already see and really drive what we see on the storage side, right? We've been showing that graph for years and years and years of just everything going up and to the right, but as AI starts working on itself and improving itself, as the collection means keep getting better and more sophisticated and have increased resolutions, whether you're talking about cameras or in life sciences, acquisition capabilities just keep getting better and better and the resolutions get better and better, it's more and more data, right? And you want to be able to expose a wide variety of data to these algorithms; that's how they're going to learn faster. And so what we see is that the data-centric part of the infrastructure is going to need to scale, even if you're starting today with a smaller workload. >> Kurt Kuckein, DDN, thanks very much for being on The Cube. >> Thanks for having me. >> And once again, this is Peter Burris with another Cube Conversation, thank you very much for watching. Until next time. (electronic whooshing)