>>Good afternoon everyone, and thank you so much for joining us. My name is Savannah Peterson, joined by my co-host Paul for the afternoon. Very excited. Oh, Savannah. Hello. I'm, I'm pumped for this. This is our first bit together. Exactly. >>It's gonna be fun. Yes. We have a great guest to kick off with. >>We absolutely do. We're at Supercomputing 2022 today, and very excited to talk to our next guest. We're gonna be talking about data at scale and data that really matters to us joining us. Kelly Gayer, thank you so much for being here and you are with tech. Tell everyone what TAC is. >>Tech is the Texas Advanced Computing Center at the University of Texas at Austin. And thank you so much for having me here. >>It is wonderful to have you. Your smile's contagious. And one of the themes that's come up a lot with all of our guests, and we just talked about it, is how good it is to be back in person, how good it is to be around our hardware, community tech. You did some very interesting research during the pandemic. Can you tell us about that? >>I can. I did. So when we realized sort of mid-March, we realized that, that this was really not normal times and the pandemic was statement. Yes. That pandemic was really gonna touch everyone. I think a lot of us at the center and me personally, we dropped everything to plug in and that's what we do. So UT's tagline is what starts here changes the world and tax tagline is powering discoveries that change the world. So we're all about impact, but I plugged in with the research group there at UT Austin, Dr. Lauren Myers, who's an epidemiologist, and just we figured out how to plug in and compute so that we could predict the spread of, of Covid 19. >>And you did that through the use of mobility data, cell phone signals. Tell us more about what exactly you were choreographing. >>Yeah, so that was really interesting. Safe graph during the pandemic made their mobility data. Typically it was used for marketing purposes to know who was going into Walmart. The offenses >>For advertising. >>Absolutely, yeah. They made all of their mobility data available for free to people who were doing research and plugging in trying to understand Covid. 19, I picked that data up and we used it as a proxy for human behavior. So we knew we had some idea, we got weekly mobility updates, but it was really mobility all day long, you know, anonymized. I didn't know who they were by cell phones across the US by census block group or zip code if we wanted to look at it that way. And we could see how people were moving around. We knew what their neighbor, their home neighborhoods were. We knew how they were traveling or not traveling. We knew where people were congregating, and we could get some idea of, of how people were behaving. Were they really, were they really locking down or were they moving in their neighborhoods or were they going outside of their neighborhoods? >>What a, what a fascinating window into our pandemic lives. So now that you were able to do this for this pandemic, as we look forward, what have you learned? How quickly could we forecast? What's the prognosis? >>Yeah, so we, we learned a tremendous amount. I think during the pandemic we were reacting, we were really trying. It was a, it was an interesting time as a scientist, we were reacting to things almost as if the earth was moving underneath us every single day. So it was something new every day. And I've told people since I've, I haven't, I haven't worked that hard since I was a graduate student. So it was really daylight to dark 24 7 for a long period of time because it was so important. And we knew, we, we knew we were, we were being a part of history and affecting something that was gonna make a difference for a really long time. And, and I think what we've learned is that indeed there is a lot of data being collected that we can use for good. We can really understand if we get organized and we get set up, we can use this data as a means of perhaps predicting our next pandemic or our next outbreak of whatever. It is almost like using it as a canary in the coal mine. There's a lot in human behavior we can use, given >>All the politicization of, of this last pandemic, knowing what we know now, making us better prepared in theory for the next one. How confident are you that at least in the US we will respond proactively and, and effectively when the next one comes around? >>Yeah, I mean, that's a, that's a great question and, and I certainly understand why you ask. I think in my experience as a scientist, certainly at tech, the more transparent you are with what you do and the more you explain things. Again, during the pandemic, things were shifting so rapidly we were reacting and doing the best that we could. And I think one thing we did right was we admitted where we felt uncertain. And that's important. You have to really be transparent to the general public. I, I don't know how well people are gonna react. I think if we have time to prepare, to communicate and always be really transparent about it. I think those are three factors that go into really increasing people's trust. >>I think you nailed it. And, and especially during times of chaos and disaster, you don't know who to trust or what to believe. And it sounds like, you know, providing a transparent source of truth is, is so critical. How do you protect the sensitive data that you're working with? I know it's a top priority for you and the team. >>It is, it is. And we, we've adopted the medical mantra, do no harm. So we have, we feel a great responsibility there. There's, you know, two things that you have to really keep in mind when you've got sensitive data. One is the physical protection of it. And so that's, that's governed by rule, federal rules, hipaa, ferpa, whatever, whatever kind of data that you have. So we certainly focus on the physical protection of it, but there's also sort of the ethical protection of it. What, what is the quote? There's lies, damn lies and statistics. >>Yes. Twain. >>Yeah. So you, you really have to be responsible with what you're doing with the data, how you're portraying the results. And again, I think it comes back to transparency is is basically if people are gonna reproduce what I did, I have to be really transparent with what I did. >>I, yeah, I think that's super important. And one of the themes with, with HPC that we've been talking about a lot too is, you know, do people trust ai? Do they trust all the data that's going into these systems? And I love that you just talked about the storytelling aspect of that, because there is a duty, it's not, you can cut data kind of however you want. I mean, I come from marketing background and we can massage it to, to do whatever we want. So in addition to being the deputy director at Tech, you are also the DEI officer. And diversity I know is important to you probably both as an individual, but also in the work that you're doing. Talk to us about that. >>Yeah, I mean, I, I very passionate about diversity, equity and inclusion in a sense of belongingness. I think that's one of the key aspects of it. Core >>Of community too. >>I got a computer science degree back in the eighties. I was akin to a unicorn in a, in an engineering computer science department. And, but I was really lucky in a couple of respects. I had a, I had a father that was into science that told me I could do anything I, I wanted to set my mind to do. So that was my whole life, was really having that support system. >>He was cheers to dad. >>Yeah. Oh yeah. And my mom as well, actually, you know, they were educators. I grew up, you know, in that respect, very, very privileged, but it was still really hard to make it. And I couldn't have told you back in that time why I made it and, and others didn't, why they dropped out. But I made it a mission probably back, gosh, maybe 10, 15 years ago, that I was really gonna do all that I could to change the needle. And it turns out that there are a number of things that you can do grassroots. There are certainly best practices. There are rules and there are things that you really, you know, best practices to follow to make people feel more included in an organization, to feel like they belong it, shared mission. But there are also clever things that you can do with programming to really engage students, to meet people and students where they are interested and where they are engaged. And I think that's what, that's what we've done over, you know, the course of our programming over the course of about maybe since 2016. We have built a lot of programming ATAC that really focuses on that as well, because I'm determined the needle is gonna change before it's all said and done. It just really has to. >>So what, what progress have you made and what goals have you set in this area? >>Yeah, that, that's a great question. So, you know, at first I was a little bit reluctant to set concrete goals because I really didn't know what we could accomplish. I really wasn't sure what grassroots efforts was gonna be able to, you're >>So honest, you can tell how transparent you are with the data as well. That's >>Great. Yeah, I mean, if I really, most of the successful work that I've done is both a scientist and in the education and outreach space is really trust relationships. If I break that trust, I'm done. I'm no longer effective. So yeah, I am really transparent about it. But, but what we did was, you know, the first thing we did was we counted, you know, to the extent that we could, what does the current picture look like? Let's be honest about it. Start where we are. Yep. It was not a pretty picture. I mean, we knew that anecdotally it was not gonna be a great picture, but we put it out there and we leaned into it. We said, this is what it is. We, you know, I hesitated to say we're gonna look 10% better next year because I'm, I'm gonna be honest, I don't always know we're gonna do our best. >>The things that I think we did really well was that we stopped to take time to talk and find out what people were interested in. It's almost like being present and listening. My grandmother had a saying, you have two errors in one mouth for a reason, just respect the ratio. Oh, I love that. Yeah. And I think it's just been building relationships, building trust, really focusing on making a difference, making it a priority. And I think now what we're doing is we've been successful in pockets of people in the center and we are, we are getting everybody on board. There's, there's something everyone can do, >>But the problem you're addressing doesn't begin in college. It begins much, much, that's right. And there's been a lot of talk about STEM education, particularly for girls, how they're pushed out of the system early on. Also for, for people of color. Do you see meaningful progress being made there now after years of, of lip service? >>I do. I do. But it is, again, grassroots. We do have a, a, a researcher who was a former teacher at the center, Carol Fletcher, who is doing research and for CS for all we know that the workforce, so if you work from the current workforce, her projected workforce backwards, we know that digital skills of some kind are gonna be needed. We also know we have a, a, a shortage. There's debate on how large that shortage is, but about roughly about 1 million unmet jobs was projected in 2020. It hasn't gotten a lot better. We can work that problem backwards. So what we do there is a little, like a scatter shot approach. We know that people come in all forms, all shapes, all sizes. They get interested for all different kinds of reasons. We expanded our set of pathways so that we can get them where they can get on to the path all the way back K through 12, that's Carol's work. Rosie Gomez at the center is doing sort of the undergraduate space. We've got Don Hunter that does it, middle school, high school space. So we are working all parts of the problem. I am pretty passionate about what we consider opportunity youth people who never had the opportunity to go to college. Is there a way that we can skill them and get, get them engaged in some aspect and perhaps get them into this workforce. >>I love that you're starting off so young. So give us an example of one of those programs. What are you talking to kindergartners about when it comes to CS education? >>You know, I mean, gaming. Yes. Right. It's what everybody can wrap their head around. So most kids have had some sort of gaming device. You talk in the context, in the context of something they understand. I'm not gonna talk to them about high performance computing. It, it would go right over their heads. And I think, yeah, you know, I, I'll go back to something that you said Paul, about, you know, girls were pushed out. I don't know that girls are being pushed out. I think girls aren't interested and things that are being presented and I think they, I >>Think you're generous. >>Yeah. I mean, I was a young girl and I don't know why I stayed. Well, I do know why I stayed with it because I had a father that saw something in me and I had people at critical points in my life that saw something in me that I didn't see. But I think if we ch, if we change the way we teach it, maybe in your words they don't get pushed out or they, or they won't lose interest. There's, there's some sort of computing in everything we do. Well, >>Absolutely. There's also the bro culture, which begins at a very early >>Age. Yeah, that's a different problem. Yeah. That's just having boys in the classroom. Absolutely. You got >>It. That's a whole nother case. >>That's a whole other thing. >>Last question for you, when we are sitting here, well actually I've got, it's two parter, let's put it that way. Is there a tool or something you wish you could flick a magic wand that would make your job easier? Where you, you know, is there, can you identify the, the linchpin in the DEI challenge? Or is it all still prototyping and iterating to figure out the best fit? >>Yeah, that is a, that's a wonderful question. I can tell you what I get frustrated with is that, that >>Counts >>Is that I, I feel like a lot of people don't fully understand the level of effort and engagement it takes to do something meaningful. The >>Commitment to a program, >>The commitment to a program. Totally agree. It's, there is no one and done. No. And in fact, if I do that, I will lose them forever. They'll be, they will, they will be lost in the space forever. Rather. The engagement is really sort of time intensive. It's relationship intensive, but there's a lot of follow up too. And the, the amount of funding that goes into this space really is not, it, it, it's not equal to the amount of time and effort that it really takes. And I think, you know, I think what you work in this space, you realize that what you gain is, is really more of, it's, it really feels good to make a difference in somebody's life, but it's really hard to do on a shoer budget. So if I could kind of wave a magic wand, yes, I would increase understanding. I would get people to understand that it's all of our responsibility. Yes, everybody is needed to make the difference and I would increase the funding that goes to the programs. >>I think that's awesome, Kelly, thank you for that. You all heard that. More funding for diversity, equity, and inclusion. Please Paul, thank you for a fantastic interview, Kelly. Hopefully everyone is now inspired to check out tac perhaps become a, a Longhorn, hook 'em and, and come deal with some of the most important data that we have going through our systems and predicting the future of our pandemics. Ladies and gentlemen, thank you for joining us online. We are here in Dallas, Texas at Supercomputing. My name is Savannah Peterson and I look forward to seeing you for our next segment.

(contemplative upbeat music) >> Hello and welcome to this CUBE Conversation. I'm John Furrier, your host of theCUBE, here in Palo Alto, California. Got a great topic on expanding capabilities for urgent computing. Dan Stanzione, he's Executive Director of TACC, the Texas Advanced Computing Center, and Rajesh Pohani, VP of PowerEdge, HPC Core Compute at Dell Technologies. Gentlemen, welcome to this CUBE Conversation. >> Thanks, John. >> Thanks, John, good to be here. >> Rajesh, you got a lot of computing in PowerEdge, HPC, Core Computing. I mean, I get a sense that you love compute, so we'll jump right into it. And of course, I got to love TACC, Texas Advanced Computing Center. I can imagine a lot of stuff going on there. Let's start with TACC. What is the Texas Advanced Computing Center? Tell us a little bit about that. >> Yeah, we're part of the University of Texas at Austin here, and we build large-scale supercomputers, data systems, AI systems, to support open science research. And we're mainly funded by the National Science Foundation, so we support research projects in all fields of science, all around the country and around the world. Actually, several thousand projects at the moment. >> But tied to the university, got a lot of gear, got a lot of compute, got a lot of cool stuff going on. What's the coolest thing you got going on right now? >> Well, for me, it's always the next machine, but I think science-wise, it's the machines we have. We just finished deploying Lonestar6, which is our latest supercomputer, in conjunction with Dell. A little over 600 nodes of those PowerEdge servers that Rajesh builds for us. Which makes more than 20,000 that we've had here over the years, of those boxes. But that one just went into production. We're designing new systems for a few years from now, where we'll be even larger. Our Frontera system was top five in the world two years ago, just fell out of the top 10. So we've got to fix that and build the new top-10 system sometime soon. We always have a ton going on in large-scale computing. >> Well, I want to get to the Lonestar6 in a minute, on the next talk track, but... What are some of the areas that you guys are working on that are making an impact? Take us through, and we talked before we came on camera about, obviously, the academic affiliation, but also there's a real societal impact of the work you're doing. What are some of the key areas that the TACC is making an impact? >> So there's really a huge range from new microprocessors, new materials design, photovoltaics, climate modeling, basic science and astrophysics, and quantum mechanics, and things like that. But I think the nearest-term impacts that people see are what we call urgent computing, which is one of the drivers around Lonestar and some other recent expansions that we've done. And that's things like, there's a hurricane coming, exactly where is it going to land? Can we refine the area where there's going to be either high winds or storm surge? Can we assess the damage from digital imagery afterwards? Can we direct first responders in the optimal routes? Similarly for earthquakes, and a lot recently, as you might imagine, around COVID. In 2020, we moved almost a third of our resources to doing COVID work, full-time. >> Rajesh, I want to get your thoughts on this, because Dave Vellante and I have been talking about this on theCUBE recently, a lot. Obviously, people see what cloud's, going on with the cloud technology, but compute and on-premises, private cloud's been growing. If you look at the hyperscale on-premises and the edge, if you include that in, you're seeing a lot more user consumption on-premises, and now, with 5G, you got edge, you mentioned first responders, Dan. This is now pointing to a new architectural shift. As the VP of PowerEdge and HPC and Core Compute, you got to look at this and go, "Hmm." If Compute's going to be everywhere, and in locations, you got to have that compute. How does that all work together? And how do you do advanced computing, when you have these urgent needs, as well as real-time in a new architecture? >> Yeah, John, I mean, it's a pretty interesting time when you think about some of the changing dynamics and how customers are utilizing Compute in the compute needs in the industry. Seeing a couple of big trends. One, the distribution of Compute outside of the data center, 5G is really accelerating that, and then you're generating so much data, whether what you do with it, the insights that come out of it, that we're seeing more and more push to AI, ML, inside the data center. Dan mentioned what he's doing at TACC with computational analysis and some of the work that they're doing. So what you're seeing is, now, this push that data in the data center and what you do with it, while data is being created out at the edge. And it's actually this interesting dichotomy that we're beginning to see. Dan mentioned some of the work that they're doing in medical and on COVID research. Even at Dell, we're making cycles available for COVID research using our Zenith cluster, that's located in our HPC and AI Innovation Lab. And we continue to partner with organizations like TACC and others on research activities to continue to learn about the virus, how it mutates, and then how you treat it. So if you think about all the things, and data that's getting created, you're seeing that distribution and it's really leading to some really cool innovations going forward. >> Yeah, I want to get to that COVID research, but first, you mentioned a few words I want to get out there. You mentioned Lonestar6. Okay, so first, what is Lonestar6, then we'll get into the system aspect of it. Take us through what that definition is, what is Lonestar6? >> Well, as Dan mentioned, Lonestar6 is a Dell technology system that we developed with TACC, it's located at the University of Texas at Austin. It consists of more than 800 Dell PowerEdge 6525 servers that are powered with 3rd Generation AMD EPYC processors. And just to give you an example of the scale of this cluster, it could perform roughly three quadrillion operations per second. That's three petaFLOPS, and to match what Lonestar6 can compute in one second, a person would have to do one calculation every second for a hundred million years. So it's quite a good-size system, and quite a powerful one as well. >> Dan, what's the role that the system plays, you've got petaFLOPS, what, three petaFLOPS, you mentioned? That's a lot of FLOPS! So obviously urgent computing, what's cranking through the system there? Take us through, what's it like? >> Sure, well, there there's a mix of workloads on it, and on all our systems. So there's the urgent computing work, right? Fast turnaround, near real-time, whether it's COVID research, or doing... Project now where we bring in MRI data and are doing sort of patient-specific dosing for radiation treatments and chemotherapy, tailored to your tumor, instead of just the sort of general for people your size. That all requires sort of real-time turnaround. There's a lot AI research going on now, we're incorporating AI in traditional science and engineering research. And that uses an awful lot of data, but also consumes a huge amount of cycles in training those models. And then there's all of our traditional, simulation-based workloads and materials and digital twins for aircraft and aircraft design, and more efficient combustion in more efficient photovoltaic materials, or photovoltaic materials without using as much lead, and things like that. And I'm sure I'm missing dozens of other topics, 'cause, like I said, that one really runs every field of science. We've really focused the Lonestar line of systems, and this is obviously the sixth one we built, around our sort of Texas-centric users. It's the UT Austin users, and then with contributions from Texas A&M , and Texas Tech and the University of Texas system, MD Anderson Healthcare Center, the University of North Texas. So users all around the state, and every research problem that you might imagine, those are into. We're just ramping up a project in disaster information systems, that's looking at the probabilities of flooding in coastal Texas and doing... Can we make building code changes to mitigate impact? Do we have to change the standard foundation heights for new construction, to mitigate the increasing storm surges from these sort of slow storms that sit there and rain, like hurricanes didn't used to, but seem to be doing more and more. All those problems will run on Lonestar, and on all the systems to come, yeah. >> It's interesting, you mentioned urgent computing, I love that term because it could be an event, it could be some slow kind of brewing event like that rain example you mentioned. It could also be, obviously, with the healthcare, and you mentioned COVID earlier. These are urgent, societal challenges, and having that available, the processing capability, the compute, the data. You mentioned digital twins. I can imagine all this new goodness coming from that. Compare that, where we were 10 years ago. I mean, just from a mind-blowing standpoint, you have, have come so far, take us through, try to give a context to the level of where we are now, to do this kind of work, and where we were years ago. Can you give us a feel for that? >> Sure, there's a lot of ways to look at that, and how the technology's changed, how we operate around those things, and then sort of what our capabilities are. I think one of the big, first, urgent computing things for us, where we sort of realized we had to adapt to this model of computing was about 15 years ago with the big BP Gulf Oil spill. And suddenly, we were dumping thousands of processors of load to figure out where that oil spill was going to go, and how to do mitigation, and what the potential impacts were, and where you need to put your containment, and things like that. And it was, well, at that point we thought of it as sort of a rare event. There was another one, that I think was the first real urgent computing one, where the space shuttle was in orbit, and they knew something had hit it during takeoff. And we were modeling, along with NASA and a bunch of supercomputers around the world, the heat shield and could they make reentry safely? You have until they come back to get that problem done, you don't have months or years to really investigate that. And so, what we've sort of learned through some of those, the Japanese tsunami was another one, there have been so many over the years, is that one, these sort of disasters are all the time, right? One thing or another, right? If we're not doing hurricanes, we're doing wildfires and drought threat, if it's not COVID. We got good and ready for COVID through SARS and through the swine flu and through HIV work, and things like that. So it's that we can do the computing very fast, but you need to know how to do the work, right? So we've spent a lot of time, not only being able to deliver the computing quickly, but having the data in place, and having the code in place, and having people who know the methods who know how to use big computers, right? That's been a lot of what the COVID Consortium, the White House COVID Consortium, has been about over the last few years. And we're actually trying to modify that nationally into a strategic computing reserve, where we're ready to go after these problems, where we've run drills, right? And if there's a, there's a train that derails, and there's a chemical spill, and it's near a major city, we have the tools and the data in place to do wind modeling, and we have the terrain ready to go. And all those sorts of things that you need to have to be ready. So we've really sort of changed our sort of preparedness and operational model around urgent computing in the last 10 years. Also, just the way we scheduled the system, the ability to sort of segregate between these long-running workflows for things that are really important, like we displaced a lot of cancer research to do COVID research. And cancer's still important, but it's less likely that we're going to make an impact in the next two months, right? So we have to shuffle how we operate things and then just, having all that additional capacity. And I think one of the things that's really changed in the models is our ability to use AI, to sort of adroitly steer our simulations, or prune the space when we're searching parameters for simulations. So we have the operational changes, the system changes, and then things like adding AI on the scientific side, since we have the capacity to do that kind of things now, all feed into our sort of preparedness for this kind of stuff. >> Dan, you got me sold, I want to come work with you. Come on, can I join the team over there? It sounds exciting. >> Come on down! We always need good folks around here, so. (laughs) >> Rajesh, when I- >> Almost 200 now, and we're always growing. >> Rajesh, when I hear the stories about kind of the evolution, kind of where the state of the art is, you almost see the innovation trajectory, right? The growth and the learning, adding machine learning only extends out more capabilities. But also, Dan's kind of pointing out this kind of response, rapid compute engine, that they could actually deploy with learnings, and then software, so is this a model where anyone can call up and get some cycles to, say, power an autonomous vehicle, or, hey, I want to point the machinery and the cycles at something? Is the service, do you guys see this going that direction, or... Because this sounds really, really good. >> Yeah, I mean, one thing that Dan talked about was, it's not just the compute, it's also having the right algorithms, the software, the code, right? The ability to learn. So I think when those are set up, yeah. I mean, the ability to digitally simulate in any number of industries and areas, advances the pace of innovation, reduces the time to market of whatever a customer is trying to do or research, or even vaccines or other healthcare things. If you can reduce that time through the leverage of compute on doing digital simulations, it just makes things better for society or for whatever it is that we're trying to do, in a particular industry. >> I think the idea of instrumenting stuff is here forever, and also simulations, whether it's digital twins, and doing these kinds of real-time models. Isn't really much of a guess, so I think this is a huge, historic moment. But you guys are pushing the envelope here, at University of Texas and at TACC. It's not just research, you guys got real examples. So where do you guys see this going next? I see space, big compute areas that might need some data to be cranked out. You got cybersecurity, you got healthcare, you mentioned oil spill, you got oil and gas, I mean, you got industry, you got climate change. I mean, there's so much to tackle. What's next? >> Absolutely, and I think, the appetite for computing cycles isn't going anywhere, right? And it's only going to, it's going to grow without bound, essentially. And AI, while in some ways it reduces the amount of computing we do, it's also brought this whole new domain of modeling to a bunch of fields that weren't traditionally computational, right? We used to just do engineering, physics, chemistry, were all super computational, but then we got into genome sequencers and imaging and a whole bunch of data, and that made biology computational. And with AI, now we're making things like the behavior of human society and things, computational problems, right? So there's this sort of growing amount of workload that is, in one way or another, computational, and getting bigger and bigger. So that's going to keep on growing. I think the trick is not only going to be growing the computation, but growing the software and the people along with it, because we have amazing capabilities that we can bring to bear. We don't have enough people to hit all of them at once. And so, that's probably going to be the next frontier in growing out both our AI and simulation capability, is the human element of it. >> It's interesting, when you think about society, right? If the things become too predictable, what does a democracy even look like? If you know the election's going to be over two years from now in the United States, or you look at these major, major waves >> Human companies don't know. >> of innovation, you say, "Hmm." So it's democracy, AI, maybe there's an algorithm for checking up on the AI 'cause biases... So, again, there's so many use cases that just come out of this. It's incredible. >> Yeah, and bias in AI is something that we worry about and we work on, and on task forces where we're working on that particular problem, because the AI is going to take... Is based on... Especially when you look at a deep learning model, it's 100% a product of the data you show it, right? So if you show it a biased data set, it's going to have biased results. And it's not anything intrinsic about the computer or the personality, the AI, it's just data mining, right? In essence, right, it's learning from data. And if you show it all images of one particular outcome, it's going to assume that's always the outcome, right? It just has no choice, but to see that. So how we deal with bias, how do we deal with confirmation, right? I mean, in addition, you have to recognize, if you haven't, if it gets data it's never seen before, how do you know it's not wrong, right? So there's about data quality and quality assurance and quality checking around AI. And that's where, especially in scientific research, we use what's starting to be called things like physics-informed or physics-constrained AI, where the neural net that you're using to design an aircraft still has to follow basic physical laws in its output, right? Or if you're doing some materials or astrophysics, you still have to obey conservation of mass, right? So I can't say, well, if you just apply negative mass on this other side and positive mass on this side, everything works out right for stable flight. 'Cause we can't do negative mass, right? So you have to constrain it in the real world. So this notion of how we bring in the laws of physics and constrain your AI to what's possible is also a big part of the sort of AI research going forward. >> You know, Dan, you just, to me just encapsulate the science that's still out there, that's needed. Computer science, social science, material science, kind of all converging right now. >> Yeah, engineering, yeah, >> Engineering, science, >> slipstreams, >> it's all there, >> physics, yeah, mmhmm. >> it's not just code. And, Rajesh, data. You mentioned data, the more data you have, the better the AI. We have a world what's going from silos to open control planes. We have to get to a world. This is a cultural shift we're seeing, what's your thoughts? >> Well, it is, in that, the ability to drive predictive analysis based on the data is going to drive different behaviors, right? Different social behaviors for cultural impacts. But I think the point that Dan made about bias, right, it's only as good as the code that's written and the way that the data is actually brought into the system. So making sure that that is done in a way that generates the right kind of outcome, that allows you to use that in a predictive manner, becomes critically important. If it is biased, you're going to lose credibility in a lot of that analysis that comes out of it. So I think that becomes critically important, but overall, I mean, if you think about the way compute is, it's becoming pervasive. It's not just in selected industries as damage, and it's now applying to everything that you do, right? Whether it is getting you more tailored recommendations for your purchasing, right? You have better options that way. You don't have to sift through a lot of different ideas that, as you scroll online. It's tailoring now to some of your habits and what you're looking for. So that becomes an incredible time-saver for people to be able to get what they want in a way that they want it. And then you look at the way it impacts other industries and development innovation, and it just continues to scale and scale and scale. >> Well, I think the work that you guys are doing together is scratching the surface of the future, which is digital business. It's about data, it's about out all these new things. It's about advanced computing meets the right algorithms for the right purpose. And it's a really amazing operation you guys got over there. Dan, great to hear the stories. It's very provocative, very enticing to just want to jump in and hang out. But I got to do theCUBE day job here, but congratulations on success. Rajesh, great to see you and thanks for coming on theCUBE. >> Thanks for having us, John. >> Okay. >> Thanks very much. >> Great conversation around urgent computing, as computing becomes so much more important, bigger problems and opportunities are around the corner. And this is theCUBE, we're documenting it all here. I'm John Furrier, your host. Thanks for watching. (contemplative music)

>>from the Cube Studios in >>Palo Alto and Boston connecting with thought leaders all around the world. This is a cube conversation. Hi. I'm stupid, man. And welcome to a cube conversation. I'm coming to you from our Boston area studio. Happy to welcome to the program. First time guest on the program. Francis Mattis. He is the vice president of engineering at Pensando. Francis. Thanks so much for joining us. >>Thank you. Good to be here. All >>right. So, Frances, you and I actually overlapped. Ah, you know, some of the companies who work with, you know, if anybody familiar with Pensando, you have worked with some of the mpls team over the years through some of those spin ins, but for our audience, give us a little bit about your background. You know, what brought you to help and be part of the team that you started pensando? >>Sure. Yeah. Yeah. So I started my career with Advanced Micro Devices in the mid nineties, got out of school, really wanted to build micro processors. And so, Andy, being in Austin, Texas, and be going to ls you for undergrad was perfect sort of alignment. And so I got to say M. D and Austin built K five worked on that team or kind of team with K seven. And, uh, when I came out to California to help with K, and that brought me to California. And then we got into the dot com era and and being a A and B fighting intel, so to speak, seemed like a hard battle. And so, with the dot com era coming, I just saw this perfect opportunity to jump into the Internet. And so that's how we got into building Internet and data communications equipment, went to the show on systems. We talked a little bit about that earlier, and that got me into storage. From there, I got into a company called on GMO, which was building fibre channel sand equipment. So built chips there, and I got to know the Mpls team there. I always say they hired me off the street. And from that point on, while we've been together since Jews 1001 So 19 years, yeah. Yeah, and I've been building silicon with them and systems for almost 20 years now. So we had quite a journey. Yeah, it's been fun. Great >>stuff. Yeah, you know it's going back, you know, niche on talking about ice scuzzy. You know, in the networking world, you know, it's a little bit of a dark arts in general for most people, you know, understanding the networking protocols and all the various pieces and three and four letter acronyms aren't something that most people are familiar with. Pensando, I'm curious. You know what? You know, networking In general, you're like, I work on Internet stuff and we're the tubes that, you know, Things go around. So when when you describe pensando, you know how to explain that to the people that maybe aren't deep into East, west, south, over on under underlay protocols? >>Yeah, absolutely. So for me, pensando was kind of the sort of the culmination of all the things I've done in my career processing, you know, being able to build compute engines that have programmable, starting with microprocessors, being able to do storage and storage networking with Andy on no, we build a computer with druva and the virtualization layers around the Ethernet interfaces in the adapter with what was really our first smart nick, Um, in 6 4007 timeframe and then with STN in CNI, all of these elements kind of came together. These multiple different layers in the infrastructure stack, if you will, and so pensando for me. What was interesting was the explosion of scale in both space and time with the advent of, let's say, 25 gig 50 gig 100 gig to the server, the notion of very dense computing on in each rack and the need for very high scale After doing all of these technologies and seeing where silicon kind of started to fall in place, I was 16 centimeter. It seemed that bringing this kind of technology to the edge very low power with sort of an end to end security architecture and to end policy engine architecture, distributed services as we're doing all seem to naturally fit into place. And the cloud was already proving this morning when I say the cloud, I mean, the hyper scaler is like Amazon and Microsoft. We are already building these platforms. And so yeah, it dawned on me that, uh I didn't think this was possible unless you built the entire platform. We built the entire system. If you build any one piece, the market transition would take a lot longer. And I think this is true. In technology, history tends to repeat itself, starting with mainframes. When IBM built an entire computer and that built the entire computer, HP built these people. So these kinds of things, um, are important if you want to really push a market transition. And so pensando became this opportunity to take all of these things that I've done in my past life and bring them together in a way that would give a complete stack for the purposes of what I call the new computer, which is basically the data center. And so, um, you know, when my mom asks me, you know, what is it that you're doing? I said, Well, it's just imagine the computer you have right now and multiplying by thousands and thousands stacking in Iraq, and anyone can use it at any one time. And we provide the infrastructure and the mechanisms to be able to Teoh, orchestrate and control that very, very high speed layers. So I don't know if that was a long answer. >>No, no, no. It's fascinating stuff, and you know, when I look at the industry, you know cloud. Of course. Is that just make a wave? That changed the way a lot of people look at this. The way we architect things, there was this belief for a number of years. Well, you know, I'm going to go from this complicated mess that I had in my own data centers and cloud was going to be, you know, inexpensive and easy. And I don't think anybody thinks about inexpensive and easy when they look at cloud computing these days, then add edge into these environments. So I guess what I'm asking is, you know, today's environment, you know, we know I t always is additive. So I have various pieces that I need to put together. You talked about building platforms, and how can it be a complete stack? So companies like Oracle, you know, for many years said we can do everything from the silicon all the way up through your application. Amazon in many ways does the same thing they can. You can build everything on Amazon, but they built out their ecosystem. So how does Pensando fit into this? You know, multi cloud, multi dimensional multi vendor. >>So yeah, so that's a good question. so So one of the things we wanted to do is to be able to bring a systematic management layer two header Genius, beauty. And what I mean by that is in any enterprise data center, modern data center, you're gonna have multiple types of computing. You're gonna have virtual machines, you're gonna have their metal, and you're gonna have containers, or at least in the last, say, three or four years. Chances are you'll have some containers and moving there. And so what we wanted to do was be able to Brighton Infrastructure a management mechanism where all of these head Virginia's types of computing could be managed the same way with respect to policy. What I mean by policy is sort of this declarative or intent based model of I have declared what I'd like to see, whether that the network policy or and and security with data in motion and be able to plot apply it in a distributed manner. Across these different types of hetero genius elements, the cloud has the advantage that it's homogenous for the most part. I mean, they own the entire infrastructure and they can control everything on their now our systems will obviously manage the marginal systems as well, and in many ways that's easier. But bringing together these this notion of heterogeneity these types of computing with one management plane one type of interface for the operator, specifically the networking services operator, was fundamental. That and then the second thing is being able to bring the scale and speed to the edge. So a top of rack switch or something in the in the middle of the network is obviously very dense in terms of this Iot capability. So the silicon area that you spend building a high speed switch is really spent for the most part on the Iot, unless typically, 30 to 40% of the area will be Iot and the rest will be very much hardwired control protocols. We know that as we go to STN services and we want, uh, let's say software defined mechanisms in terms of what the policy looks like, what the protocols look like. The ability to change over time in the lifespan of the computer, which is 3 to 5 years, are you want that to be programmable, very difficult to apply a very dense scale in the core of the network. And so it was an obvious move to bring that to the edge where we could plug it into the server effectively, just like we did. Really? In the UCS system. Uh, no system. >>Yeah, some some really tough engineering challenges. You know, for the longest time, it was very predictable in the networking world, You know, you go from one gig to 10 gig. You know, there was a little discussion how we went the next step, whether, you know, 25 50 40 and 100 gig now. But you talk about containerized architectures. You talk about distributed systems with edge. Things change at a much smaller granular level and change much more frequently. So what are some of the design principles and challenges that you make sure that you're ready for what's happening today but also knowing that, you know, technology changes there always coming, and you need to be able to handle, You know, that next thing. Yeah, >>that's right. Yes. So, uh, I think part of the biggest challenges we have are around power with respect to design power. And then what is the usefulness of each transistor? So, um, when you you have sort of a scale of flexibility. See, views are the most flexible, obviously, but have probably the least performance in them. PG A's are pretty useful in terms of its flexibility, but not very dense in terms of its logic capability. And then you have hardwired a six, which are extremely dense, very much purpose built logic, but completely inflexible. And so the design challenge it was put in front of us is how do we find that sweet spot of extremely programmable, extremely flexible, but still having a cost profile that didn't look like an F PGA And God knows the benefits of the CPU. And and that's where this sort of this notion of domain specific processing came in, which is okay, well, if we're going to solve a few problems, we're going to solve them well. And those few problems are going to be we're gonna bring PC services. We're going to bring networking services. We're going to bring stories, services. We're gonna bring security services around the edge of the computer so that we can offload or let's say, partition correctly the computing problem in a data center. And to do that, we knew a core of sea views wasn't going to do a job that's basically borrowing from this guy to pay this other guy. Right? So what we wanted to do was bring this notion of domain specific processing, and that's where our design challenges came in, which is okay, So now we build around this language called P four, What is the most optimal way to pack? The most amount of threads are processing elements into the silicon while managing the memory bandwidth, which is obviously, you know, packet processing is it has been said to be embarrassingly parallel, which is true. However, the memory bandwidth is insane. And so how do we build a system that insurance that memory is not the bottleneck? Obviously, we're producing a lot of data or, uh, computing a lot of data. And so So these were some of our design challenges. All of that within a power envelope where this part of this device could sit at the edge inside of a computer within a typical power profiling by PC, a attached card in a modern computer. So that was a huge design challenge for us. >>Yeah, I'd love to hear, you know, it was a multi year journey toe solution. And I think of the old World. It was very much a hardware centric 18 to 24 months for design and all the tape out you need to do on this. Sounds like obviously there is still hardware, but it is more software driven. Then it would have been, you know, 10 years ago. So give us some of the ups and downs in that journey. Love to hear any. Any stories that you can share their Well, yeah, I >>think you know, good question. It's always there's always ups and downs in anything you do, especially in the start up. And I think one of the biggest challenges we we've faced is, uh, the exact hardware software boundary. So what is it that you want in hardware? What is it that you want in software And, uh, you know, one of the greatest assets and our company depends on who are the people. We have amazing software and hardware architects who work extremely well together because most of us have been together for so long. So, um, so that always helps when you start to partition the problem. We spent the first year of Pensando, which was basically 2017. The company was founded really thinking through this problem, would it for for all the problems, we wanted to solve the goals that were given to us and and security. Okay, so I want to be able to terminate TCP and initiate TLS connections. What's the right architecture for that? I want to be able to do storage off load and be able to provide encryption of data at rest data in motion. I want to be able to do compression these kinds of things. What's the right part of our software boundary for that? What do we what do we hardwire in silicon versus what we make it programmable and silicon, obviously, but still through a computing engine. And so we spent the first year of the company really thinking through those different partitioning problems, and that was definitely a challenge. And we spent a lot of time and and, uh, you helped me conference rooms and white boards figuring that out. And then 2018. The challenge there was now taking this architecture, this sort of technology substrate, if you will that we built and then executing on it, making sure that it was actually going to yield what we hope that would that we would be able to provide the services. When we talk about El four firewall at line rate, that's completely programmable. Uh, we achieved that. Can we do load balancing? And we do all of it with this before processing engine and the innovations we brought before satisfy all of these requirements we put for us. And so 2018 was really about execution. And there you always have. The challenge is in execution. In terms of, you know, things are going to go wrong. It's not. It's not. If it's when and then how do you deal with it? And so again, um, I would say the biggest challenge and execution is, uh, containing the changes. You know, it's so easy for things to change, especially when you're trying to really build a software platform right, because it's always easy to sort of kick the can and say we'll deal with that later and software. But we know that given what we're trying to do, which is build a system that is highly performance, um, you can't get that. Can you have to deal with it when it comes in. So we spend a lot of time doing performance analysis, making sure that all these applications we were building we're going t yield the right performance. And so that was quite a challenge. And then 2019 was kind of the year of shaping the product. Really lots of product design. Okay, now that we have this technology and it does these, he says that we wanted to do these pieces meaning services. What are all the different ways we can shake this product after talking to customers for, you know, months and months and months. You know, Sony is very much custom, customer driven customer centric. So we we were fortunate enough that we got to spend a lot of time with customers and then that brings us out of challenges, right? Because every customer has a unique problems and so I don't know how to reform this product around a solution that solves quite a bit of problems that really brings value. And so that was the those are the challenges in 2019 which we overcame. Now, obviously we have several releases that we've come out with already. We've got a six and the chips and the It's all there now. So now, 2020. Unfortunately, covitz here, But this is this is a year of growth. This is the year that we really bring it out into the world with our partners and our customers and show how this technology has been developed and benefit will benefit customers over over the next years. Two years. >>Frances really appreciate the insight there. Yeah, that that discussion of the hardware versus software brings back memories for May. Lots of heated debates. A CIO What? One of lines you know we've used on the Cube many times is you know, you know, software will eventually work. Hardware will eventually break. So those trade rto >>taught me something over time ago. He said that uh huh, hardware is hard to change. Software is hard to stop changing. So >>that that's a great one to All right, So you gave us through the last three years journey. Give us a little bit. Look, you know, on the next three years and where you expect pensando to be going >>Sure. Where I see pensando in the next three years as we go through this market transition is uh, both a market leader in a thought leader in terms of the next wave of data center edge computing, whether the, uh in the service provider space, whether it be in the enterprise space or whether it be in the cloud space, the hyper hyper scale of space. As I was mentioning in the beginning, we had when we were talking about, uh, the journey. Market transitions of this major really require understanding the entire stack. If you provide a piece and someone else provides a piece, you will eventually get there. But it's a matter of when, and by the time you get there, there's probably something new. So, you know, uh, time in and of itself is an innovation in this area, especially when you're dealing with the market transition like this. And so we've been fortunate enough that we're building the entire system when we go from the transistors to the rest of the FBI's way, have the entire staff. And so where I see us in three years is not only being a market leader in this space, but also being a thought leader in terms of what does domain specific processing look like at the edge. Um, you know, what are the tools? What are the techniques for? Really a z save? Democratizing the cloud bringing, bringing this technology to everyone. >>Excellent. Well, hey, Frances, That has been a pleasure to talk with you. Thank you so much. Congratulations on the journey so far and I can't wait to see you. How? Thanks for going >>forward. Yeah, we're excited, and I appreciate it. Thank you for your time to. All >>right, check out the cube dot net. We've got lots of back catalogue with pensando. Also, I'm stew minimum. And thank you for watching the Q. Yeah, yeah, yeah.

>>artificial intelligence. The words are full of possibility. Yet to many it may seem complex, expensive and hard to know where to get started. How do you make AI really for your business? At Dell Technologies, we see AI enhancing business, enriching lives and improving the world. Dell Technologies is dedicated to making AI easy, so more people can use it to make a real difference. So you can adopt and run AI anywhere with your current skill. Sets with AI Solutions powered by power edge servers and made portable across hybrid multi clouds with VM ware. Plus solved I O bottlenecks with breakthrough performance delivered by Dell EMC Ready solutions for HPC storage and Data Accelerator. And enjoy automated, effortless management with open manage systems management so you can keep business insights flowing across a multi cloud environment. With an AI portfolio that spans from workstations to supercomputers, Dell Technologies can help you get started with AI easily and grow seamlessly. AI has the potential to profoundly change our lives with Dell Technologies. AI is easy to adopt, easy to manage and easy to scale. And there's nothing artificial about that. Yeah, yeah, from >>the Cube Studios in Palo Alto and Boston >>connecting with >>thought leaders all around the world. This is a cube conversation. Hi, I'm Stew Minimum. And welcome to this special launch with our friends at Dell Technologies. We're gonna be talking about AI and the reality of making artificial intelligence real happy to welcome to the program. Two of our Cube alumni Rob, depending 90. He's the senior vice president of server product management and very Pellegrino vice president, data centric workloads and solutions in high performance computing, both with Dell Technologies. Thank you both for joining thanks to you. So you know, is the industry we watch? You know, the AI has been this huge buzz word, but one of things I've actually liked about one of the differences about what I see when I listen to the vendor community talking about AI versus what I saw too much in the big data world is you know, it used to be, you know Oh, there was the opportunity. And data is so important. Yes, that's really But it was. It was a very wonky conversation. And the promise and the translation of what has been to the real world didn't necessarily always connect and We saw many of the big data solutions, you know, failed over time with AI on. And I've seen this in meetings from Dell talking about, you know, the business outcomes in general overall in i t. But you know how ai is helping make things real. So maybe we can start there for another product announcements and things we're gonna get into. But Robbie Interior talk to us a little bit about you know, the customers that you've been seeing in the impact that AI is having on their business. >>Sure, Teoh, I'll take us a job in it. A couple of things. For example, if you start looking at, uh, you know, the autonomous vehicles industry of the manufacturing industry where people are building better tools for anything they need to do on their manufacturing both. For example, uh, this is a good example of where that honors makers and stuff you've got Xeon ut It's actually a world war balcony. Now it is using our whole product suite right from the hardware and software to do multiple iterations off, ensuring that the software and the hardware come together pretty seamlessly and more importantly, ingesting, you know, probably tens of petabytes of data to ensure that we've got the right. They're training and gardens in place. So that's a great example of how we are helping some of our customers today in ensuring that we can really meet is really in terms of moving away from just a morning scenario in something that customers are able to use like today. >>Well, if I can have one more, Ah Yanai, one of our core and more partners than just customers in Italy in the energy sector have been been really, really driving innovation with us. We just deployed a pretty large 8000 accelerator cluster with them, which is the largest commercial cluster in the world. And where they're focusing on is the digital transformation and the development of energy sources. And it's really important not be an age. You know, the plan. It's not getting younger, and we have to be really careful about the type of energies that we utilize to do what we do every day on they put a lot of innovation. We've helped set up the right solution for them, and we'll talk some more about what they've done with that cluster. Later, during our chat, but it is one of the example that is tangible with the appointment that is being used to help there. >>Great. Well, we love starting with some of the customer stories. Really glad we're gonna be able to share some of those, you know, actual here from some of the customers a little bit later in this launch. But, Robbie, you know, maybe give us a little bit as to what you're hearing from customers. You know, the overall climate in AI. You know, obviously you know, so many challenges facing, you know, people today. But you know, specifically around ai, what are some of the hurdles that they might need to overcome Be able to make ai. Really? >>I think the two important pieces I can choose to number one as much as we talk about AI machine learning. One of the biggest challenges that customers have today is ensuring that they have the right amount and the right quality of data to go out and do the analytics percent. Because if you don't do it, it's giggle garbage in garbage out. So the one of the biggest challenges our customers have today is ensuring that they have the most pristine data to go back on, and that takes quite a bit of an effort. Number two. A lot of times, I think one of the challenges they also have is having the right skill set to go out and have the execution phase of the AI pod. You know, work done. And I think those are the two big challenges we hear off. And that doesn't seem to be changing in the very near term, given the very fact that nothing Forbes recently had an article that said that less than 15% off, our customers probably are using AI machine learning today so that talks to the challenges and the opportunities ahead for me. All right, >>So, Ravi, give us the news. Tell us the updates from Dell Technologies how you're helping customers with AI today, >>going back to one of the challenges, as I mentioned, which is not having the right skin set. One of the things we are doing at Dell Technologies is making sure that we provide them not just the product but also the ready solutions that we're working with. For example, Tier and his team. We're also working on validated and things are called reference architectures. The whole idea behind this is we want to take the guesswork out for our customers and actually go ahead and destroying things that we have already tested to ensure that the integration is right. There's rightsizing attributes, so they know exactly the kind of a product that would pick up our not worry about me in time and the resources needed you get to that particular location. So those are probably the two of the biggest things we're doing to help our customers make the right decision and execute seamlessly and on time. >>Excellent. So teary, maybe give us a little bit of a broader look as to, you know, Dell's part participation in the overall ecosystem when it comes to what's happening in AI on and you know why is this a unique time for what's happening in the in the industry? >>Yeah, I mean, I think we all live it. I mean, I'm right here in my home, and I'm trying to ensure that the business continues to operate, and it's important to make sure that we're also there for our customers, right? The fight against covered 19 is eyes changing what's happening around the quarantines, etcetera. So Dell, as a participant not only in the AI the world that we live in on enabling AI is also a participant in all of the community's s. So we've recently joined the covered 19 High Performance Computing Consortium on. We also made a lot of resources available to researchers and scientists leveraging AI in order to make progress towards you're and potentially the vaccine against Corbyn. 19 examples are we have our own supercomputers in the lab here in Austin, Texas, and we've given access to some of our partners. T. Gen. Is one example. The beginning of our chat I mentioned and I So not only did they have barely deport the cluster with us earlier this year that could 19 started hitting, so they've done what's the right thing to do for community and humanity is they made the resource available to scientists in Europe on tack just down the road here, which had the largest I can't make supercomputer that we deployed with them to. Ai's doing exactly the same thing. So this is one of the real examples that are very timely, and it's it's it's happening right now we hadn't planned for it. A booth there with our customers, the other pieces. This is probably going to be a trend, but healthcare is going through and version of data you mentioned in the beginning. You're talking about 2.3000 exabytes, about 3000 times the content of the Library of Congress. It's incredible, and that data is useless. I mean, it's great we can We can put that on our great ice on storage, but you can also see it as an opportunity to get business value out of it. That's going to be we're a lot more resource is with AI so a lot happening here. That's that's really if I can get into more of the science of it because it's healthcare, because it's the industry we see now that our family members at the M. Ware, part of the Dell Technologies Portfolio, are getting even more relevance in the discussion. The industry is based on virtualization, and the M ware is the number one virtualization solution for the industry. So now we're trying to weave in the reality in the I T environment with the new nodes of AI and data science and HPC. So you will see the VM Ware just added kubernetes control plane. This fear Andi were leveraging that to have a very flexible environment On one side, we can do some data science on the other side. We can go back to running some enterprise class hardware class software on top of it. So this is is great. And we're capitalizing on it with validates solutions, validated design on. And I think that's going to be adding a lot of ah power in the hands of our customers and always based on their feedback. And they asked back, >>Yeah, I may ask you just to build on that interesting comment that you made on we're actually looking at very shortly will be talking about how we're gonna have the ability to, for example, read or V Sphere and Allah servers begin. That essentially means that we're going to cut down the time our customers need to go ahead and deploy on their sites. >>Yeah, excellent. Definitely been, you know, very strong feedback from the community. We did videos around some of the B sphere seven launch, you know, theory. You know, we actually had done an interview with you. Ah, while back at your big lab, Jeff Frick. Otto, See the supercomputers behind what you were doing. Maybe bring us in a little bit inside as who? You know, some of the new pieces that help enable AI. You know, it often gets lost on the industry. You know, it's like, Oh, yeah, well, we've got the best hardware to accelerate or enable these kind of workloads. So, you know, bring us in its But what, You know, the engineering solution sets that are helping toe make this a reality >>of today. Yeah, and truly still you've been there. You've seen the engineers in the lab, and that's more than AI being real. That that is double real because we spend a lot of time analyzing workloads customer needs. We have a lot of PhD engineers in there, and what we're working on right now is kind of the next wave of HPC enablement Azaz. We all know the consumption model or the way that we want to have access to resources is evolving from something that is directly in front of us. 1 to 1 ratio to when virtualization became more prevalent. We had a one to many ratio on genes historically have been allocated on a per user. Or sometimes it is study modified view to have more than one user GP. But with the addition of big confusion to the VM our portfolio and be treated not being part of these fear. We're building up a GPU as a service solutions through a VM ware validated design that we are launching, and that's gonna give them flexibility. And the key here is flexibility. We have the ability, as you know, with the VM Ware environment, to bring in also some security, some flexibility through moving the workloads. And let's be honest with some ties into cloud models on, we have our own set of partners. We all know that the big players in the industry to But that's all about flexibility and giving our customers what they need and what they expect in the world. But really, >>Yeah, Ravi, I guess that brings us to ah, you know, one of the key pieces we need to look at here is how do we manage across all of these environments? Uh, and you know, how does AI fit into this whole discussion between what Dell and VM ware doing things like v Sphere, you know, put pulling in new workloads >>stew, actually a couple of things. So there's really nothing artificial about the real intelligence that comes through with all that foolish intelligence we're working out. And so one of the crucial things I think we need to, you know, ensure that we talk about is it's not just about the fact that it's a problem. So here are our stories there, but I think the crucial thing is we're looking at it from an end to end perspective from everything from ensuring that we have direct workstations, right servers, the storage, making sure that is well protected and all the way to working with an ecosystem of software renders. So first and foremost, that's the whole integration piece, making sure they realized people system. But more importantly, it's also ensuring that we help our customers by taking the guess work out again. I can't emphasize the fact that there are customers who are looking at different aliens off entry, for example, somebody will be looking at an F G. A. Everybody looking at GP use. API is probably, as you know, are great because they're price points and normal. Or should I say that our needs our lot lesser than the GP use? But on the flip side, there's a need for them to have a set of folks who can actually program right. It is why it's called the no programming programmable gate arrays of Saas fee programmable. My point being in all this, it's important that we actually provide dried end to end perspective, making sure that we're able to show the integration, show the value and also provide the options, because it's really not a cookie cutter approach of where you can take a particular solution and think that it will put the needs of every single customer. He doesn't even happen in the same industry, for that matter. So the flexibility that we provide all the way to the services is truly our attempt. At Dell Technologies, you get the entire gamut of solutions available for the customer to go out and pick and choose what says their needs the best. >>Alright, well, Ravi interior Thank you so much for the update. So we're gonna turn it over to actually hear from some of your customers. Talk about the power of ai. You're from their viewpoint, how real these solutions are becoming. Love the plan words there about, you know, enabling really artificial intelligence. Thanks so much for joining after the customers looking forward to the VM Ware discussion, we want to >>put robots into the world's dullest, deadliest and dirtiest jobs. We think that if we can have machines doing the work that put people at risk than we can allow people to do better work. Dell Technologies is the foundation for a lot of the >>work that we've done here. Every single piece of software that we developed is simulated dozens >>or hundreds of thousands of times. And having reliable compute infrastructure is critical for this. Yeah, yeah, A lot of technology has >>matured to actually do something really useful that can be used by non >>experts. We try to predict one system fails. We try to predict the >>business impatience things into images. On the end of the day, it's that >>now we have machines that learn how to speak a language from from zero. Yeah, everything >>we do really, at Epsilon centered around data and our ability >>to get the right message to >>the right person at the right >>time. We apply machine learning and artificial intelligence. So in real time you can adjust those campaigns to ensure that you're getting the most optimized message theme. >>It is a joint venture between Well, cars on the Amir are your progress is automated driving on Advanced Driver Assistance Systems Centre is really based on safety on how we can actually make lives better for you. Typically gets warned on distracted in cars. If you can take those kind of situations away, it will bring the accidents down about 70 to 80%. So what I appreciate it with Dell Technologies is the overall solution that they have to live in being able to deliver the full package. That has been a major differentiator compared to your competitors. >>Yeah. Yeah, alright, welcome back to help us dig into this discussion and happy to welcome to the program Chris Facade. He is the senior vice president and general manager of the B sphere business and just Simon, chief technologist for the High performance computing group, both of them with VM ware. Gentlemen, thanks so much for joining. Thank >>you for having us. >>All right, Krish. When vm Ware made the bit fusion acquisition. Everybody was looking the You know what this will do for space Force? GPU is we're talking about things like AI and ML. So bring us up to speed. As to you know, the news today is the what being worth doing with fusion. Yeah. >>Today we have a big announcement. I'm excited to announce that, you know, we're taking the next big step in the AI ML and more than application strategy. With the launch off bit fusion, we're just now being fully integrated with VCF. They're in black home, and we'll be releasing this very shortly to the market. As you said when we acquire institution A year ago, we had a showcase that's capable days as part of the animal event. And at that time we laid out a strategy that part of our institution as the cornerstone off our capabilities in the black home in the Iot space. Since then, we have had many customers take a look at the technology and we have had feedback from them as well as from partners and analysts. And the feedback has been tremendous. >>Excellent. Well, Chris, what does this then mean for customers? You know What's the value proposition that diffusion brings the VC? Yeah, >>if you look at our customers, they are in the midst of a big ah journey in digital transformation. And basically, what that means is customers are building a ton of applications and most of those applications some kind of data analytics or machine learning embedded in it. And what this is doing is that in the harbor and infrastructure industry, this is driving a lot of innovation. So you see the advent off a lot off specialized? Absolutely. There's custom a six FPs. And of course, the views being used to accelerate the special algorithms that these AI ml type applications need. And unfortunately, customer environment. Most of these specialized accelerators uh um bare metal kind of set up, but they're not taking advantage off optimization and everything that it brings to that. Also, with fusion launched today, we are essentially doing the accelerator space. What we need to compute several years ago and that is essentially bringing organization to the accelerators. But we take it one step further, which is, you know, we use the customers the ability to pull these accelerators and essentially going to be couple it from the server so you can have a pool of these accelerators sitting in the network. And customers are able to then target their workloads and share the accelerators get better utilization by a lot of past improvements and, in essence, have a smaller pool that they can use for a whole bunch of different applications across the enterprise. That is a huge angle for our customers. And that's the tremendous positive feedback that we get getting both from customers as well. >>Excellent. Well, I'm glad we've got Josh here to dig into some of the thesis before we get to you. They got Chris. Uh, part of this announcement is the partnership of VM Ware in Dell. So tell us about what the partnership is in the solutions for for this long. Yeah. >>We have been working with the Dell in the in the AI and ML space for a long time. We have ah, good partnership there. This just takes the partnership to the next level and we will have ah, execution solution. Support in some of the key. I am el targeted words like the sea for 1 40 the r 7 40 Those are the centers that would be partnering with them on and providing solutions. >>Excellent. Eso John. You know, we've watched for a long time. You know, various technologies. Oh, it's not a fit for virtualized environment. And then, you know, VM Ware does does what it does. Make sure you know, performance is there. And make sure all the options there bring us inside a little bit. You know what this solution means for leveraging GPS? Yeah. So actually, before I before us, answer that question. Let me say that the the fusion acquisition and the diffusion technology fits into a larger strategy at VM Ware around AI and ML. That I think matches pretty nicely the overall Dell strategy as well, in the sense that we are really focused on delivering AI ml capabilities or the ability for our customers to run their am ai and ml workloads from edge before the cloud. And that means running it on CPU or running it on hardware accelerators like like G fuse. Whatever is really required by the customer in this specific case, we're quite excited about using technology as it really allows us. As Chris was describing to extend our capabilities especially in the deep learning space where GPU accelerators are critically important. And so what this technology really brings to the table is the ability to, as Chris was outlining, to pull those resources those hardware resource together and then allow organizations to drive up the utilization of those GP Resource is through that pooling and also increase the degree of sharing that we support that supported for the customer. Okay, Jeff, take us in a little bit further as how you know the mechanisms of diffusion work. Sure, Yeah, that's a great question. So think of it this way. There there is a client component that we're using a server component. The server component is running on a machine that actually has the physical GPU is installed in it. The client machine, which is running the bit fusion client software, is where the user of the data scientist is actually running their machine machine learning application. But there's no GPU actually in that host. And what is happening with fusion technology is that it is essentially intercepting the cuda calls that are being made by that machine learning app, patience and promoting those protocols over to the bit fusion server and then injecting them into the local GPU on the server. So it's actually, you know, we call it into a position in the ability that remote these protocols, but it's actually much more sophisticated than that. There are a lot of underlying capabilities that are being deployed in terms of optimization who takes maximum advantage of the the networking link that sits between the client machine and the server machine. But given all of that, once we've done it with diffusion, it's now possible for the data scientist. Either consume multiple GP use for single GPU use or even fractional defuse across that Internet using the using technology. Okay, maybe it would help illustrate some of these technologies. If you got a couple of customers, Sure, so one example would be a retail customer. I'm thinking of who is. Actually it's ah, grocery chain. That is the flowing, ah, large number of video cameras into their to their stores in order to do things like, um, watch for pilfering, uh, identify when storage store shelves could be restocked and even looking for cases where, for example, maybe a customer has fallen down in denial on someone needs to go and help those multiple video streams and then multiple app patients that are being run that part are consuming the data from those video streams and doing analytics and ml on them would be perfectly suited for this type of environment where you would like to be ableto have these multiple independent applications running but having them be able to efficiently share the hardware resources of the GP use. Another example would be retailers who are deploying ml Howard Check out registers who helped reduce fraud customers who are buying, buying things with, uh, fake barcodes, for example. So in that case, you would not necessarily want to employ a single dedicated GPU for every single check out line. Instead, what you would prefer to do is have a full set of resource. Is that each inference operation that's occurring within each one of those check out lines could then consume collectively. That would be two examples of the use of this kind of pull in technology. Okay, great. So, Josh, a lot last question for you is this technology is this only for use and anything else. You can give us a little bit of a look forward to as to what we should be expecting from the big fusion technology. Yeah. So currently, the target is specifically NVIDIA GPU use with Cuda. The team, actually even prior to acquisition, had done some work on enablement of PJs and also had done some work on open CL, which is more open standard for a device that so what you will see over time is an expansion of the diffusion capabilities to embrace devices like PJs. The domain specific a six that first was referring to earlier will roll out over time. But we are starting with the NVIDIA GPU, which totally makes sense, since that is the primary hardware acceleration and for deep learning currently excellent. Well, John and Chris, thank you so much for the updates to the audience. If you're watching this live, please throwing the crowd chat and ask your questions. This faith, If you're watching this on demand, you can also go to crowdchat dot net slash make ai really to be able to see the conversation that we had. Thanks so much for joining. >>Thank you very much. >>Thank you. Managing your data center requires around the clock. Attention Dell, EMC open manage mobile enables I t administrators to monitor data center issues and respond rapidly toe unexpected events anytime, anywhere. Open Manage Mobile provides a wealth of features within a comprehensive user interface, including >>server configuration, push notifications, remote desktop augmented reality and more. The latest release features an updated Our interface Power and Thermal Policy Review. Emergency Power Reduction, an internal storage monitoring download Open Manage Mobile today.

>> Announcer: Live from Austin Texas, it's theCUBE, covering South by Southwest 2017, brought to you by Intel. Now, here's John Furrier. >> Welcome back everyone, we are live at the Intel AI Lounge, end of the day, day one at South by Southwest, I'm John Furrier, this is theCUBE, our flagship programming brought to the events and extract a signal from the noise. What a day it is here, it's the packed venue, AI Lounge, with Intel, it's the hottest spot in South by Southwest, of course, where our theme is AI for social good, and our next guest is Bryce Olson with Intel, and your title officially is, global marketing director health and live services, but you are an amazing story, cancer survivor, but a fighter, you took it to technology to stop your cancer, and also, a composer with your friend, called FACTS, Fighting Advanced Cancer Through Song, the stories. Welcome to theCUBE! >> Thank you, it's great to be here, this is awesome, this is amazing environment that we're in today. But yeah, you're right, when you look at data, genomics data, which is looking at your DNA, and running that out and being able to understand what could potentially be fueling disease, that's the biggest of big data. And when I was working at Intel, I was in a non-healthcare oriented group, and then all of a sudden, I got hit with cancer, like very aggressive, advanced cancer. And I went through the whole standard of care, and I went through that one-size-fits-all spin that wheel of treatments and hopefully you get something kind of thing, nothing-- >> General purpose, chemotherapy, whatever, blah blah blah. >> Nothing worked. And I came to the point where I was start to come to terms with the fact that I may not see my daughter get through elementary school. So, cancer's starting to grow again, I go back to work, at this point, I only want to work in healthcare, because, why would I want to do anything else? I want to try to-- >> John: But you have terminal cancer at this point. >> I have terminal cancer at this point, but I'm not sick yet. You know, I went through all the chemo and all that crap, but I'm not sick yet. So, I asked to get into Intel's healthcare group, because I want to try to help healthcare providers make this digital transformation. They let me in, and what I found out kind of blew my mind. I learned about this new space of genomics and precision medicine. >> Well, it turns out, hold on for a second, you were telling me the story before, but you skipped a step, it turns out Intel has a lot of work going on, so you come into Intel, you're like, they open up the kimono-- >> Open up the kimono, and I learn about this new era called, just basically genomics, so what is genomics? Genomics, essentially, is a way to look at disease differently. Why can't we go in and find out what's fueling disease deep in the DNA? Because every disease is diagnosable by DNA, we just have never had the technology, and the science, combining together to get to that answer before. Now we do. So I found out that Intel is working with all these genomic sequencing companies to increase the throughput so you can actually take something that costs $2 billion dollars back in 2003, and took 10 years to do, get it down to $1,000 and do it in a day, right? So now, it democratizes sequencing, so we can look at what's fueling disease and get the data. Then I learned about Intel working with all these major bioinformatics open stores and commercial providers, the Broad Institute at MIT, Harvard, largest genomic sequencing place on the planet, about how they take that data and then analyze it, get to what is really fueling disease. And then I learn about the cool things we're doing with customers, which I could talk about, like actual hospitals. >> Well, let's hold on for a second on that, your shirt says Sequence Me, but this is really key for the audience out there listening and watching, is that, literally 10 years ago the costs were astronomical, no one could afford it. Big grants, philanthropy-funded R&D centers, now, literally, you had your genome sequenced for thousands of dollars. >> Well, so, and this is what happened, right? I learned about all this stuff that Intel's up to, and I get kind of upset. I get kind of pissed off, right? Because nobody's giving this to me. Nobody's sequencing my cancer, right? So I go back to the cancer center that I was working with, this is January 2015, turns out they were getting ready, they were perfecting their lab diagnostic test on this, it was like a perfect storm, they were ready, I wanted it, they gave it to me, turns out my cancer grows along this particular mutated pathway that we had no idea. >> So the data was, so in your DNA sequence step one, step two is you go in massive compute power, which is available, and you go look at it, and it turns out there's a nuance to your cancer that's identifiable! >> Yeah, a needle in that haystack, right? The signal in the noise, if you will, right? So there's a specific molecular abnormality, and in my case, there was a pathway that was out of control, and the reason why I say it was out of control is, the pathway was mutated, but then there's this tumor suppressor gene that's supposed to stop cancer, he's gone! So it's like a freeway of traffic-- >> So he's checked out, and all of a sudden, this is going wild, but this is cancer for everyone has their own version of this. >> Yes they do. >> So this is now a new opportunity. >> Yes! Now we understand what's fueling my unique cancer. We took data, we took technology and science, and we got to the point where we understand what's fueling my cancer. With that data, I find a clinical trial testing a new inhibitor of that pathway. >> So I just got to stop and just pause, because it's very emotional, and first of all, man, yours is an inspiration to me and everyone watching. I'm looking at some sign this year at the Intel AI booth, and it says, "Your amazing starts with Intel," this is truly an amazing story. >> Yeah, thank you. >> It's really beyond amazing, it's life saving! >> And that's what happened to me. >> This is now at the beginning, so take me through, in your mind, where is the progress bar on this, in the AI evolution, or when I say AI, I mean like machine learning, compute, end-to-end technology innovation. It's available, obviously, when is it going to be mainstream? >> Yeah, so, we're at a point right now where we can go in, if you have advanced cancer, we're at a point now where we can sequence that person's cancer and find out what's driving it, we can do that. But where it's going to get problematic is, look at my case. The mutated pathway hypersegmented by cancer, right, so prostate cancer, a common cancer, now became a rare cancer, because we hypersegmented it by DNA, and I went after a treatment that was targeted, so when my cancer starts to grow again, now I'm a rare cancer. So how are going to find people that are just like me out there in the world? >> So your point about rare being, there's no comparable data to look at benchmarking, so that's the challenge. >> Yeah, no given hospital will ever have enough data in this new molecular genomics-guided medicine world to solve my problem, because the doctors are going to want to look, and they're going to say, "Who out there looks just like Bryce "from a DNA perspective, uniquely? "What treatments were given to people like that, "and what were the outcomes?" The only way we're going to solve that is as all these centers and hospitals start amassing data, it has to work together, it has to collaborate in a way that preserves patient privacy, and also protects individual IP. >> Okay, so Bryce, let me ask you a question, if you could put a bumper sticker or a soundbite around what AI means to this evolution innovation around fighting cancer and using data and technology, what is the impact of AI to this? >> So, where I'm kind of going with this analogy is that without artificial intelligence to sift through my data, and all the other millions of potential cancer patients to start getting DNA data, humans can't do it, it's impossible, humans will not have the mental ability to sift through reams and reams of DNA data that exists for every patient out there to look at treatments and outcomes and synthesize it, we can't do it. The only way someone like me will survive into the long term will be through artificial intelligence. Without it, I will extend my life, but I won't turn cancer into a manageable disease without AI. >> So the AI will extend your life. >> Because AI is going to solve the problems that humans can't. When you have the biggest of big data-- >> Love that soundbite, love that, say that again! AI solves the problems that-- >> AI is going to solve the problems that humans can't, they simply, humans don't have the capability to look at the entire genome, and all this other genomic, molecular, proteomic, all this other data, we can't make sense of it! >> Alright, so let me throw something out at you, 'cause I agree 100%, but also, there's a humanization factor, 'cause now algorithms are also biased by humans, so what's your thoughts, given your experience, the role of the human race, actual human beings, that have a pulse, not robots or algorithms? >> Yeah, so let me give you a real practical example. So, the way that we fought my cancer was through a targeted therapy. Molecular abnormality, targeted drug. The other way that people are fighting cancer is through immunotherapy. Wake up the immune system to fight it. Guess what? Right now, there are 800 combination therapies going on with immunotherapy to try to stop people's cancer. How the heck are we going to know what is the right combination for each person out there? Unless we have like an algorithm marketplace where people are creating these, and taking in predictive biomarkers, prognostic biomarkers, looking at all the data, and then pushing a button to help an oncologist decide which of the 800 combos to use, we'll never get there. So-- >> That's awesome. So let me ask you a question, so for people watching that are younger, like my daughter, she's 16, my other daughter's a premed, she's a sophomore in college, they're like, school's like old, like, school's like linear, they get classes, but this younger generation are hungry for data, they're hungry, they want to, they're young, they're what people do, they disrupt, they're bomb throwers, they want to create value, and so their incentive to go after cancer, and the means are out there, cancer cells, we all have relatives who have died of cancer, it's a sucky situation. There's a motivated force out there of scientists, and young people. How do they get involved? How would you look at, based on your experience, and your experience, obviously, you got these songs here, but on a more practical level, what discovery, what navigation can someone take in their life to just get involved, not a catalog, not the courseware. >> I think, so there's a number of different things that can happen, if you look at the precision medicine landscape, and you start with a patient, patients don't understand this. "Genomic what? "Sequencing what?" They don't understand that there's a new way to fight cancer, so guess what's going to become a 20% per year growth rate job in the next 10 to 20 years? Genomics counselors. You don't have to be a doctor, but you have to be able to understand enough about biology-- >> And math. >> To be able to offload doctors, and have a discussion with patients to say, "Let me explain something to you. "There's a way to understand your disease, it's in DNA, "this is what it means," and then help them guide them into new clinical trials and other therapy that's got it by that, huge growth opportunity for kids. >> But also, it's compounded by the fact we just said earlier, where these become rare cases on paper, are also need to be aggregated into a database of some sort so you can understand the data, so there's also a data science angle here. >> Absolutely, and it's not just cancer, by the way, I mean, little kids in the NICU, pediatric ailments. Have you ever know anybody who's got a kid with a very rare neurodevelopmental disorder, and the parents are on a diagnostic odyssey for 10 years, they can't figure out what it is? So they go from specialist to specialist, specialist, $100,000 dollars later, guess what, the answer's in the DNA. >> DNA sequencing, number one. >> DNA sequencing, number one, and then, once you start sequencing that, you got to make sense of all this data, so there's going to be tons of jobs, not only in biology, but in analytics, to take all this data and start finding-- >> Alright, we got a few minutes left, I want to get a plugin for your little album here, it's called FACTS, Fighting Against Cancer Through Song. >> So here's the story on that. So, when you go through something that could be terminal, it's really nice when you can have something productive to channel that energy. So for me, to be able to channel feelings of sadness and frustration, I started writing songs. Music was therapeutic for me. I took that, started collaborating with a bunch of musicians throughout Portland, including cancer survivors, and we said, why don't we use music as a way to reach people about a new message of how to fight cancer? So we created that, I have an organization that is raising awareness for a new way to fight cancer, and raising funds, to bring sequencing to more people. >> So the URL is factsmovement.com, so factsmovements.com, check it out. Okay, now, I'm so impressed with you, one, you are on a terminal track, you go back to work. >> But I don't look like I'm terminal! >> You look great, you look great. Now, you're at Intel, Intel's got technology, you harness it, now, you're on a mission now, your passion, it's obvious, the songs, now, what's going on in Intel, 'cause now you're out doing the Intel thing, gives us the Intel update. >> I can talk to you about this precision medicine, it's personalizing diagnostic and treatment plan, which I've already done, I could talk to you about other things that we're doing to help hospitals transform. Predictive clinical analytics, let's look at something like rapid response teamed events. Have you ever been in the hospital and heard the alarms go off? That's usually somebody having a heart attack unexpected. Data is out there, if you look at all the data about people that have had rapid response teams events, we can create predictive signals to actually predict that an hour before it would happen! So predictive clinical analytics, and enabling hospitals to look at populations as a whole to treat them better in this new value-based care, is a technology-driven thing, so we're working on that as well. Yeah. >> Well Bryce, thanks for coming on to theCUBE, we appreciate it, really inspirational, great to meet you in person, and I'm looking forward to following up with you when you get back to Portland, we'll get our gang in Palo Alto to get you on the horn Skype in, and keep in touch, really inspirational, but more importantly, this is very relevant, and the technology's now surfacing to change, not only people's lives in the sense of saving them, but other great things. >> And I'm so proud to be able to work for a company that is using its brand and its technology to basically change people's lives, it's amazing. >> Bryce Olson, my hero here at South by Southwest, amazing story, really, really, you can choose to be a victim or you can choose to go after it, so excited to have met you, it's theCUBE, breaking it all down here at South by Southwest at Intel's AI Lounge, it's hopping, music tonight, music tomorrow night, CUBE tomorrow, panels, AI changing the future powered by Intel, #IntelAI, I'm John Furrier, you're watching theCUBE, thanks for watching, we'll see you tomorrow.