 Live from Madrid, Spain. It's theCUBE, covering HPE Discover Madrid 2017. Brought to you by Hewlett Packard Enterprise. Welcome back to Madrid, everybody. This is theCUBE, the leader in live tech coverage. My name is Dave Vellante. I'm here with my co-host Peter Burse. And this is our second day of coverage of HPE's Madrid Conference HPE Discover. Sharad Singhal is back, director of machine software and applications at HPE and works in the labs. And Mike Woodacre is here. He's a distinguished engineer for mission critical solutions at Hewlett Packard Enterprise. Gentlemen, welcome to theCUBE. Welcome back. Good to see you, Mike. Good to meet you. SuperdomeFlex is all the rage here at the show. You guys are happy about that. You were explaining off camera that that is really the first jointly engineered product from SGI and HPE. So you've hit a milestone. Yeah, no, I came into Hewlett Packard Enterprise just over a year ago with the SGI acquisition. And we're already working on our next generation in memory computing platform. But we basically hit the ground running, integrated the engineering teams immediately that we closed the acquisition. So we could drive to the finish line and with the product announcement just recently, we're really excited to get that out into the market. It really represents the sort of leading in-memory computing system in the industry. Sherrod, high-performance computing has always been big data, needing big memories, lots of performance. How has the acquisition of SGI sort of shaped your agenda in any way or your thinking or advanced some of the innovations that you guys are coming out with? Actually it was like a truly a meeting of the minds when these guys came into HPE. We had been talking about memory-driven computing, the machine prototype for the last two years. Some of us were aware of it, but a lot of us were not aware of it. But these guys had been working essentially in parallel on similar concepts. Some of the work we had done, we were thinking in terms of our roadmaps, when they were looking at the same things, their roadmaps were looking incredibly similar to what we were talking about. And as the engineering teams came about, we brought both the Superdome X technology and the UV300 technology together into this new product that Mike can talk a lot more about. From my side, I was talking about the machine and the machine research project. When I first met Mike and I started talking to him about what they were doing, my immediate reaction was, oh wow, wait a minute, this is exactly what I need, because I was talking about something where I could take the machine concepts and deliver product to customers in the 2020 time frame. And with the help of Mike and his team, we are able to now do essentially something where we can take the benefits we are describing in the machine program and make those ideas available to customers right now. And I think to me that was the fun part of this journey here. So what are the key problems that your team is attacking with this new offering? So the primary use case for the Superdome Flex is really high performance in-memory database applications, typically SAP HANA is the industry leading solution in that space right now. But one of the key things with the Superdome Flex, is the active work, it's the flexibility. You can start with a small building block, a four socket three terabyte building block, and then you just connect these boxes together, but the memory footprint just grows linearly and the latency across our fabric just stays constant as you add these modules together. So we can deliver up to 32 processors, 48 terabytes of in-memory data in a single rack. So it's really this flexibility, sort of a pay-as-you-grow model, so people can, you know, as their needs grow, they don't have to throw out the infrastructure they can add to it. So when you take a look ultimately at the combination, we talked a little bit about some of the new types of problems that can be addressed, but let's bring it practical to the average enterprise. What can the enterprise do today? As a consequence of this machine, that they couldn't do just a few weeks ago. So it sort of builds on the modularity, as I was explaining. So if you ask a CIO today, what's my database requirement going to be in two or three years? They're like, well, I hope my business is successful, I'm going to grow my needs, but I really don't know exactly where that size is going to grow. So the flexibility to just add modules and scale up the capacity of memory to bring that. So the whole concept of in-memory databases basically bringing your online, your transaction processing, and your data analytics processing together. So you can then do this in real time. So instead of your data going to a data warehouse and looking at how the business was operating days or weeks or months ago, I can see how it's acting right now with the latest updates to transact. So this is important. You mentioned two different things. Number one is you mentioned that you can envision or three things. You can start using modern technology immediately on an extremely modern platform. Number two, you can grow this and scale this as needs follow because HANA in-memory is not going to have the same scaling limitations that Oracle on a bunch of spinning disks had. But you still have the flexibility to learn and then very, very importantly, you can start adding new function, including automation, because now you can put the analytics and the transaction processing together, close that loop so that you can bring transactions, analytics, boom, into a piece of automation and scale that in unprecedented ways. So that's kind of three things that the business can now think about. If I got that right? Yeah, no, that's exactly right. And so it just, it lets people really understand how their business is operating in real time, look for trends, look for new signatures in how the business is operating so they can basically build on their success and basically having this sort of technology gives them basically a competitive advantage over their competitors. So they could basically out-compute to out-compete to get ahead of the competition. But it also presumably leads to new kinds of efficiencies because you can converge, I converge word that we've heard so much, you can not just converge the hardware and not just converge the system software and management, but you can now increasingly converge tasks and bring those tasks in the system but also at a business level down onto the same platform. Exactly, and so the moving to in memory is really about bringing real time to the problem instead of sort of batch mode processing, you bring in the real time aspects and humans were interactive, we like to ask a question, get an answer, get onto the next question in real time. That's when processes move from sort of batch mode to real time, you just get a step change in the innovation that can occur. So we think with this foundation we're really enabling the industry to step forward. So let's create a practical example here. Let's apply this platform to a sizable system that's looking at customer behavior patterns. And then let's imagine how we can take the e-commerce systems that's actually handling order, bill, remediate, fulfillment, all those other things. We can bring those two things together not just in a way that might work if we have someone online for five minutes, but right now, is that kind of one of those examples that we're looking at? Absolutely, you can basically, you know, you have a history of the customers you're working with. You know, in retail, when you go in a store the store will know your history of transactions with them. They can decide if they want to offer you real-time discounts on particular items. They'll also be taking in other data, you know, weather conditions to drive their business. You know, if suddenly there's going to be a heat wave, you know, I want more ice cream in the store or it's going to be freezing next week I'm going to order in more coats and mittens for everyone to buy. So by taking in, you know, lots of transactional data not just the actual business transaction but environmental data, you can just exhilarate your ability to provide consumers with the things they will need. Okay, so I remember when you guys launched Apollo. Antonio Neary was running the server division he might have had networking too and he did like a little reveal on the floor. So Antonio was actually in the house over there and you can see him next door. And there was an astronaut at the reveal we covered it on the cube. And so he's always been very, you know, focused on this part of the business where the high performance computing and obviously the machine has been a huge project. So how has the leadership been? You know, we got a lot of, like you said, skeptics early on said, you're crazy. What was the conversation like with Meg and Antonio? Were they continuously supportive? Were they sometimes skeptical too? What was that like? So if you think about the total amount of effort we have put in the machine program and truly speaking, that kind of effort would not be possible if the senior leadership was not behind us inside this company, right? A large fraction of us in HP labs were working on it and it was not just a labs project it was a project where our business partners were working on it. We brought together engineering teams from the business groups who understood how products were put together. We had software people working with us who were working inside the business. We had researchers from labs working with it. We had supply chain partners working with us inside this project. And a project of this scale and scope does not succeed if it's a handful of researchers doing this work. So we had enormous support from the business side and from our leadership team. So I give enormous credit to our leadership team to allow us to do this because it's an industry thing. It was not just a H Hewlett Packard enterprise thing. At the same time, with this kind of investment there is clearly an expectation that we will make it real. And it's taken us three years to go from, here is a vague idea from some group of crazy people in labs to something which actually works and is real. And frankly the conversation in the last six months have been okay, so how do we actually take it to customers? And that's where the partnership with Mike and his team has come in so valuable because at this point in time we have a shared vision of where we need to take the thing. We have something where we can onboard customers right now. We have something where frankly when I'm working on the examples we were talking about earlier today, not everybody can afford a 16 socket giant machine. The Superdome Flex on the other hand allows my customer or anybody who's playing with an application to start small, something that is reasonably affordable, try their applications out. And if their application is working, they have the ability to scale up, right? And this is what makes the Superdome Flex such a nice environment to work in for the kinds of applications I'm worrying about because it takes something which, when we had started this program, we said, people ask us when will the machine product be? And from day one we have said the machine product will be something that might become available to you in some form or another by the end of the decade. Well, I'm making it, certainly with Mike, I think I can make it happen right now and it's not quite the end of the decade, right? So I think that's what excites me about this partnership we have with the Superdome Flex team, the fact that they had a same vision and the same aspirations that we do. It's a platform that allows my current customers with their current applications, like Mike described within the context of, say, SAP HANA, a scalable platform they can operate at now, but it's also something that allows them to involve towards the future and start putting new applications that they haven't even thought about today, that those were the kinds of applications we were talking about, makes it possible for them to move into this journey today. So what is the availability of Superdome Flex? Can I buy it today or? You can buy it today, yeah. Actually, I had the pleasure of installing the first early access system in the UK last week. We've been delivering large memory platforms to Stephen Hawking's team at Cambridge University for the last 20 years because they really like the in-memory capability to allow them, as they say, to be scientists, not computer scientists in working through their algorithms and data. So, yeah, it's ready for sale today. What's going on with Hawking's team? I don't know if this was fake news or not, but I saw something come across that said, he says the world's going to blow up in 600 years. I said, uh-oh, what's Hawking got going now? That's got to be fun working with those guys. Yeah, no, it's been fun working with that team, but actually what I would say in following up on Sharad's comment was, it's been really fun this last year because I'd sort of been following the machine from outside when the announcements were made a couple of years ago. And so, immediately when the acquisition closed, I was like, tell me about the software you've been developing, tell me about the photonics and all these technologies because boy, I can now accelerate where I want to go with the technology we've been developing. So, Superdome Flex really is the first step on the path. It's a better product than either company could have delivered on their own. And now, over time, we can integrate other learnings and technologies from the machine research program. So, it's a really exciting time. Excellent, I always loved the SGI acquisition. Thought it made a lot of sense. Great brand, kind of putting SGI back in the map in a lot of ways. But gentlemen, thanks very much for coming on theCUBE. Thank you, okay. Appreciate you seeing it. Thank you. Thanks for coming on. All right, keep it right there, everybody. We'll be back with our next guest right after this short break. This is theCUBE live from HPE Discover Madrid. Be right back.