>>live from San Francisco. It's the Q covering Micron Insight 2019. Brought to you by Micron. >>Welcome back to Pier 27 sentences. You're beautiful day here. You're watching the Cube, the leader in live tech coverage recovering micron inside 2019 hashtag micron in sight. My co host, David Floy er and I are pleased to welcome Michael Wood, Acre Cube alum and a fellow at Hewlett Packard Enterprise. Michael, good to see you again. Thanks. Coming on. >>Thanks for having me. >>So you're welcome? So you're talking about HBC on a panel today? But of course, your role inside of HP is is a wider scope. Talk about that a little bit. >>She also I'm the lead technologists in our Compute Solutions business unit that pack out Enterprise. So I've come from the group that worked on in memory computing the Superdome flex platform around things like traditional enterprise computing s it, Hannah. But I'm now responsible not only for that mission critical solutions platform, but also looking at our blades and edge line businesses. Well said broader technology. >>Okay. And then, of course, today we're talking a lot about data, the growth of data and As you say, you're sitting on a panel talking about high performance computing and the impact on science. What are you seeing? One of the big trends in terms of the intersection between data in the collision with H. P. C and science. >>So what we're seeing is just this explosion of data and this really move from traditionally science of space around how you put equations into supercomputers. Run simulations. You test your theories out, look at results. >>Come back in a couple weeks, >>exactly a potential years. Now. We're seeing a lot of work around collecting data from instruments or whether it's genomic analysis, satellite observations of the planner or of the universe. These aerial generating data in vast quantities, very high rates. And so we need to rethink how we're doing our science to gain insights from this massive data increase with seeing, >>you know, when we first started covering the 10th year, the Cuban So in 2010 if you could look at the high performance computing market as sort of an indicator of some of the things that were gonna happen in so called big data, and some of those things have played out on I think it probably still is a harbinger. I wonder, how are you seeing machine intelligence applied to all this data? And what can we learn from that? In your opinion, in terms of its commercial applications. >>So a CZ we'll know this massive data explosion is how do we gain insights from this data? And so, as I mentioned, we serve equations of things like computational fluid dynamics. But now things are progressing, so we need to use other techniques to gain understanding. And so we're using artificial intelligence and particularly today, deep learning techniques to basically gain insights from the state of Wei. Don't have equations that we can use to mind this information. So we're using these aye aye techniques to effectively generate the algorithms that can. Then you bring patterns of interest to our you know, focused of them, really understand what is the scientific phenomenon that's driving the things particular pattern we're seeing within the data? So it's just beyond the ability of the number of HPC programmers, we have the sort of traditional equation based methodologies algorithms to gain insight. We're moving into this world where way just have outstripped knowledge and capabilities to gain insight. >>So So how does that? How is that being made possible? What are the differences in the architecture that you've had to put in, for example, to make this sort of thing possible? >>Yeah, it's it's really interesting time, actually, a few years ago seemed like computing was starting to get boring because wears. Now we've got this explosion of new hardware devices being built, basically moving into the more of a hetero genius. Well, because we have this expo exponential growth of data. But traditional computing techniques are slowing down, so people are looking at exaggerate er's to close that gap and all sorts of hatred genius devices. So we've really been thinking. How do we change that? The whole computing infrastructure to move from a compute centric world to a memory centric world? And how can we use memory driven computing techniques to close that gap to gain insight, so kind of rethinking the whole architectural direction basically merge, sort of collapsing down the traditional hierarchy you have, from storage to memory to the CPU to get rid of the legacy bottlenecks in converting protocols from process of memory storage down to just a simple basically memory driven architecture where you have access to the entire data set you're looking at, which could be many terabytes to pad of eyes to exabytes that you can do simple programming. Just directly load store to that huge data set to gain insights. So that's that's really changed. >>Fascinating, isn't it? So it's the Gen Z. The hope of Gen Z is actually taking place now. >>Yes, so Gen Z is an industry led consulting around a memory fabric and the, you know, Hewlett Packard Enterprise Onda whole host of industry partners, a part of the ecosystem looking at building a memory fabric where people can bring different innovations to operate, whether it's processing types, memory types, that having that common infrastructure. I mean, there's other work to in the industry the Compute Express Link Consortium. So there's a lot of interest now in getting memory semantics out of the process, er into a common fabric for people to innovate. >>Do you have some examples of where this is making a difference now, from from the work in the H B and your commercial work? >>Certainly. Yeah, we're working with customers in areas like precision medicine, genomex basically exaggerating the ability to gain insights into you know what medical pathway to go on for a particular disease were working in cybersecurity. Looking at how you know, we're worried about security of our data and things like network intrusion. So we're looking at How can you gain insights not only into known attacking patterns on a network that the unknown patents that just appearing? So we're actually a flying machine learning techniques on sort of graft data to understand those things. So there's there's really a very broad spectrum where you can apply these techniques to Data Analytics >>are all scientists now, data scientists. And what's the relationship between sort of a classic data scientist, where you think of somebody with stats and math and maybe a little bit of voting expertise and a scientist that has much more domain expertise you're seeing? You see, data scientists sort of traversed domains. How are those two worlds coming together? >>It's funny you mentioned I had that exact conversation with one of the members of the Cosmos Group in Cambridge is the Stephen Hawking's cosmology team, and he said, actually, he realized a couple of years ago, maybe he should call himself a day two scientists not cosmologist, because it seemed like what he was doing was exactly what you said. It's all about understanding their case. They're taking their theoretical ideas about the early universe, taking the day to measurements from from surveys of the sky, the background, the cosmic background radiation and trying to pair these together. So I think your data science is tremendously important. Right now. Thio exhilarate you as they are insights into data. But it's not without you can't really do in isolation because a day two scientists in isolation is just pointing out peaks or troughs trends. But how do you relate that to the underlying scientific phenomenon? So you you need experts in whatever the area you're looking at data to work with, data scientists to really reach that gap. >>Well, with all this data and all this performance, computing capacity and almost all its members will be fascinating to see what kind of insights come out in the next 10 years. Michael, thanks so much for coming on. The Cube is great to have you. >>Thank you very much. >>You're welcome. And thank you for watching. Everybody will be right back at Micron Insight 2019 from San Francisco. You're watching the Cube

>> Man: Live from Madrid, Spain, it's the Cube! Covering HPE Discover Madrid, 2017. Brought to you by: Hewlett Packard Enterprise. >> Welcome back to Madrid, everybody, this is The Cube, the leader in live tech coverage. My name is Dave Vellante, I'm here with my co-host, Peter Burris, 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, HPE and Corps and Labs >> Good to be back. And Mike Woodacre is here, a distinguished engineer from Mission Critical Solutions at Hewlett-Packard Enterprise. Gentlemen, welcome to the Cube, welcome back. Good to see you, Mike. >> Good to be here. >> Superdome Flex is all the rage here! (laughs) At this show. You guys are happy about that? You were explaining off-camera that is the first jointly-engineered product from SGI and HPE, so you hit a milestone. >> Yeah, and I came into Hewett Packard Enterprise just over a year ago with the SGI Acquisition. We're already working on our next generation in memory computing platform. We basically hit the ground running, integrated the engineering teams immediately that we closed the acquisition so we could drive through the finish line and with the product announcement just recently, we're really excited to get that out into the market. Really represent the leading in memory, computing system in the industry. >> Sharad, a high performance computer, you've always been big data, needing big memories, lots of performance... How has, or has, the acquisition of SGI shaped your agenda in any way or your thinking, or advanced some of the innovations that you guys are coming up with? >> Actually, it was truly like 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. 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 road maps and they were looking at the same things. Their road maps were looking incredibly similar to what we were talking about. 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!" I was talking about something where I could take the machine concepts and deliver products to customers in the 2020 time frame. 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. 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? >> The primary use case for the Superdome Flex is really high-performance in memory database applications, typically SAP Hana is sort of the industry leading solution in that space right now. One of the key things with the Superdome Flex, you know, Flex is the active word, it's the flexibility. You can start with a small building block of four socket, three terabyte building block, and then you just connect these boxes together. The memory footprint just grows linearly. The latency across our fabric just stays constant as you add these modules together. We can deliver up to 32 processes, 48 terabytes of in-memory data in a single rack. So it's really the flexibility, sort of a pay as you grow model. 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 Lance explained. If you ask a CEO today, "what's my database requirement going to be in two or three years?" they're like, "I hope my business is successful, I hope I'm gonna grow my needs," but I really don't know where that side 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 is basically bringing your online transaction processing and your data-analytics processing together. So then you can do this in real time and instead of your data going to a data warehouse and looking at how the business is operating days or weeks or months ago, I can see how it's acting right now with the latest updates of transactions. >> So this is important. You mentioned two different things. Number one is you mentioned 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 gonna have the same scaling limitations that you know, Oracle on a bunch of spinning discs had. >> Mike: Exactly. >> So, you still have the flexibility to learn and then very importantly, you can start adding new functions, including automation, because now you can put the analytics and the transaction processing together, close that loop so you can bring transactions, analytics, boom, into a piece of automation, and scale that in unprecedented ways. That's kind of three things that the business can now think about. Have I got that right? >> Yeah, that's exactly right. 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. They can basically build on their success and basically having this sort of technology gives them a competitive advantage over their competitors so they can out-compute or out-compete and get ahead of the competition. >> But it also presumably leads to new kinds of efficiencies because you can converge, that converge word that we've heard so much. You can not just converge the hardware and converge the system software management, but you can now increasingly converge tasks. Bring those tasks in the system, but also at a business level, down onto the same platform. >> Exactly, and so moving in memory is really about bringing real time to the problem instead of batch mode processing, you bring in the real-time aspect. Humans, we're interactive, we like to ask a question, get an answer, get on to the next question in real time. When processes move from batch mode to real time, you just get a step change in the innovation that can occur. 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 sizeable system that's looking at customer behavior patterns. Then let's imagine how we can take the e-commerce system that's actually handling order, bill, fulfillment and 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 have a history of the customers you're working with. 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, weather conditions to drive their business. Suddenly there's going to be a heat wave, I want more ice cream in the store, or it's gonna be freezing next week, I'm gonna order in more coats and mittens for everyone to buy. So taking in lots of transactional data, not just the actual business transaction, but environmental data, you can accelerate your ability to provide consumers with the things they will need. >> Okay, so I remember when you guys launched Apollo. Antonio Neri was running the server division, you might have had networking to him. He did a little reveal on the floor. Antonio's actually in the house over there. >> Mike: (laughs) Next door. There was an astronaut at the reveal. We covered it on the Cube. He's always been very focused on this part of the business of the high-performance computing, and obviously the machine has been a huge project. How has the leadership been? We had a lot of skeptics early on that said you were 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've 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 lot of us in HP labs were working on it. 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 projects were put together. We had software people working with us who were working inside the business, we had researchers from labs working, we had supply chain partners working with us inside this project. A project of this scale and scope does not succeed if it's a handful of researchers doing this work. We had enormous support from the business side and from our leadership team. I give enormous thanks to our leadership team to allow us to do this, because it's an industry thing, not just an HP Enterprise thing. At the same time, with this kind of investment, there's clearly an expectation that we will make it real. It's taken us three years to go from, "here is a vague idea from a group of crazy people in labs," to something which actually works and is real. Frankly, the conversation in the last six months has been, "okay, so how do we actually take it to customers?" That's where the partnership with Mike and his team has become so valuable. At this point in time, we have a shared vision of where we need to take the thing. We have something where we can on-board customers right now. We have something where, frankly, even I'm working on the examples we were talking about earlier today. Not everybody can afford a 16-socket, giant machine. The Superdome Flex allows my customer, or anybody who is playing with an application to start small, something that is reasonably affordable, try that application out. If that application is working, they have the ability to scale up. This is what makes the Superdome Flex such a nice environment to work in for the types of applications I'm worrying about because it takes something which when we had started this program, people would ask us, "when will the machine product be?" From day one, we 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, suddenly with Mike, I think I can make it happen right now. It's not quite the end of the decade yet, right? So I think that's what excited me about this partnership we have with the Superdome Flex team. The fact that they had the 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, SAB Hana, a scalable platform, they can operate it now. 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. Those were the kinds of applications we were talking about. It makes it possible for them to move into this journey today. >> So what is the availability of Superdome Flex? Can I buy it today? >> Mike: You can buy it today. 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 twenty 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. Yeah, it's ready for sale today. >> What's going on with Hawking's team? I don't know if this is fake news or not, but I saw something come across that said he says the world's gonna blow up in 600 years. (laughter) I was like, uh-oh, what's Hawking got going now? (laughs) That's gotta be fun working with those guys. >> Yeah, I know, it's been fun working with that team. Actually, what I would say following up on Sharad's comment, it's been really fun this last year, because I've sort of been following the machine from outside when the announcements were made a couple of years ago. 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. Superdome Flex is really the first step on the path. It's a better product than either company could have delivered on their own. Now over time, we can integrate other learnings and technologies from the machine research program. It's a really exciting time. >> Excellent. Gentlemen, I always love the SGI acquisitions. Thought it made a lot of sense. Great brand, kind of put SGI back on the map in a lot of ways. Gentlemen, thanks very much for coming on the Cube. >> Thank you again. >> We appreciate you. >> Mike: Thank you. >> Thanks for coming on. Alright everybody, We'll be back with our next guest right after this short break. This is the Cube, live from HGE Discover Madrid. Be right back. (energetic synth)