>>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)

>> Hello everyone. Welcome to this CUBE conversations here in Palo Alto, California John Furrier, host of the Cube. I'm here with Jagane Sundar CTO chief technology officer of WANdisco, you get great to see you again. Place we're coming on. >> Thank you for having me, John. >> So the conversation I want to talk to about the technology behind WANdisco and we've had many conversations. So for the folks watching good, our YouTube channel insurgency the evolution of conversations over, I think. Eight, eight, nine years now we've been chatting. What a level up. You guys are now with cloud big announcements around multi cloud live data in particular. So the technology is the gift that keeps giving for WANdisco you guys continuing to take territory now, a big way with cloud, big growth, A lot of changes, a lot of hires. What's going on? >> So, as you well know, WANdisco stands for wide area network distributed, computing on the value ofthe the wide data network aspect is really shining through now because nobody goes to the cloud saying, I'm going to put it in one data center. It's always multiple regions, multiple data centers in each region. Suddenly, problem of having your data consistent, being across multiple cloud windows are on prem to cloud becomes a real challenge. We stepped in. We had something that was a good solution for small users, small data. But we developed it into something that's fantastic for large data volumes on people are running into the problem. The biggest problem that IT providers have is that data scientists do not respect data that's not consistent. If you look at a replica of data and you're not sure whether it's exactly accurate or not the data scientists who spent all his time building algorithms to predict some model gonna look at it and go, that data's not quite right. I'm not going to look at it. So if you use a inconsistent tool or an inadequate tool to replicate your data, you have the problem that nobody is going to respect the replicas. Everybody's going to go back to the source of truth. We solved that problem elegantly and accurately >> State the problem specifically. Is it the integrity of the data? What is the specific problem statement that you guys solve with technology? >> Let me give you an exam you have notifications that come out of cloud object stores when an object this place into the store or deleted from the store that the best effort delivery. If there are logjams in this mechanism used to deliver some notifications, maybe drop the problem with using that notification mechanism to replicate your data is that over a period of time, so you have two three petabytes of data and you're replicating it over a month or month and a half, you'll find that maybe point one percent of your data is not quite accurate anymore. So the value ofthe the replicas essentially zero >> like a leaky pipe. Basically, >> indeed, if you have a leaking pipe, then it's just totally >> we need to have integrity and to end. All right, let's get back to some of the things I want to ask because I think it's a fascinating been following your story. For years, you had a point solution. Multiple wider. You had the replication active, active great for data centers. So disaster recovery not mission critical, but certainly critical. Correct, depending on how it the mission of us. It wasn't this asked Income's Cloud. You mentioned a wide area. Networks and you go back to the old days when I was breaking into the business. That's when they had, you know, dial up modems and front pagers. Not even cell phones. Just starting. Why do your network would have really complicated beast and all the best resource is worked on expensive bandwith, that he had remote offices and you had campus networking then. So why the area networking went through that phase one? Correct. Now we're living in. They win all the time. Cloud is when white area >> correct cloud is when. But there are subtle aspect that people miss all the time. If you go to store an object in Amazon, says three, for example, you pick a region. If it's a complete wide area distributed entity, why do you need to pick a region? The truth is, each cloud vendor hides a number of region specific or local area network specific aspects of their service. Dynamo DB runs and one data centre one one region, two or three availability zones in a region. If you want to replicate that data, you don't really have much help from the cloud vendor themselves. So you need to parse the truth from what has offered what you will find us. The van is still a very challenging problem for a lot of these data application problems. >> Talk about the wide area network challenges in the modern era we're living in, which is cloud computing mentioned some of the nuances around regions and availability zones. Basically, the cloud grew up as building blocks and the plumbing on the neither essentially a mai britt of of certain techniques and networking. Local area network V lands tunneling All these stuff Nets router. So it's obviously plumbing. Yes, what's different now that's important to take that to the next level. Because, you know, there are arguments that saying, Hey, GPR, I might want to have certain regions be smarter, right? So you're starting to see a level up that Amazon and others air going. Google, in particular, talks about this a lot as Ama's Microsoft. What's that next level of when, where the plumbing it's upgraded from basically the other things. >> So the problem really has to be stated in terms ofthe your data architecture. If you look at your data on, figure out that you need the set of data to be available for your business critical applications, then the problem turns into. I need replicas of this data in this region and the other reasons, perhaps in two different cloud render locations because you don't want to be tied down to their availability. One cloud vendor, then the problem tones into How do you hide the complexity of replicating and keeping this data consistent from the users of the data data scientists, the application authors and so on. Now, that's where we step in. We have a transparent replication solution that fits into the plumbing. It's often offered by the IT folks as part of their cloud offering or as part of the hybrid offering. The application. Developers don't really need to worry about those things. A specific example would be hive tables that are users building in one data center an IT Professional from that organization can buy our replication software. That table will be available in multiple data centers and multiple regions available for both Read and write. The user did not do anything or does not need to be a there. So if you have problems such as GDPR requires the data to be here. But this summarized data can be available across all of these regions. Then we can solve the problem elegantly for you without any act application rewiring or reauthoring. >> Talk about the technology that makes all this happen again. This has been a key part of your success that WANdisco love the always love the name wide area there was a big wide area that were fan did that in my early days configuring router tables. You know how it has been. You know, hardcore back then, Distributed systems is certainly large. Scale now is part of the clouds. So all the large scale guys like me when we grew up into computer science days had to think about systems, architecture at scale. We're actually living it now, Correct. So talk about the technology. What specifically do you guys have that that that's your technology and talk about the impact to the scale piece. I think that's a real key technology piece >> indeed. So the core of our algorithm is enhancements and superior implementation. Often algorithm called paxos. Now paxos itself is the only mathematically proven algorithm for keeping replicas in multiple machines or multiple regions. So multiple data centers the other alternatives. Such a raft and zookeeper protocol. These are all compromises for the sake of the ease of implementation. Now we don't feel the cost of implementation. We spent many years doing the research on it, so we have fantastic implementation. Of paxos is extended for use over wide data networks without any special hardware I mentioned without any special hardware piece, because Google Spanner, which is one of our primary competitors, has an implementation that that needs your own specific network and hardware. So the value of >> because they're tired, the clock, atomic clock, actually, to the infrastructure of their timings, that's all synchronized. So it's it's only within Google Cloud? >> Exactly. It cannot even be made available to Google's customers of Google Cloud. That was a feature that they added recently, but it's rolling out in very limited. >> They inherited that from their large scale correct Google. Yes, which is a big table spanner. These are awesome products. >> These are awesome products, but they're very specific >>Tailored for Google. >> Yes, they're great in the Google environment. They're not so great outside of Google. Now we have technology that makes you able to run this across a Google Cloud and Microsoft's Cloud and Amazons Cloud. The value of this is that you have truly cloud neutral solutions. You don't need to worry about when the lock in, you don't need to worry about availability problems in one of the cloud vendors and then you can scale your solution. You can go in with an approach such that when the virtual machines or the compute resource is in one cloud vendor are really inexpensive. Will use that when it's very expensive. Will move our workloads to other locations. You can think up architectures like that, with our solution underpinning your replication >> rights again. I'm gonna ask you the technical quite love these conversations get down and dirty on the hood. So Joel Horowitz was on your new CMO former Microsoft. Keep alumni Richard CEO Talk aboutthe. Same thing. Moving data around the key value probably that's tied right into your legacy of your I P and how that value is with integrity. Moving data from point A to point B. But the world's moving also to identify scenarios where I'm going to move compute rather than through the day, because people have recognized that moving data is hard you got late in C and this cost in band with so two schools of thought not mutually exclusive. When do you pick one? >> Okay, absolutely. They're not mutually exclusive because there are data availability needs that defined some replication scenarios on their computer needs that can be more flexible. If you had the ability to say, have data in Amazon's cloud on in Microsoft's Cloud, You mean Want to use some Amazon specific tools for specific computer scenarios at the same time, used Microsoft tools for other scenarios or perhaps use open source, too, like Hadoop in either one of those clouds? Those are all mechanisms that work perfectly well, but at the core you have to figure out your data architecture. If you can live with your data in one region or in one data center, clearly that's what you should do. But if you cannot have that data, be unavailable, you do have to replicate it. At that point, you should consider replicating to a different cloud window because availability is concerned with all these vendors. >> So two things I hear you say one availability is it's a driver. The other one is user preference Yes. Why not have people who know Microsoft tools and Microsoft software work on Microsoft framework of someone using something else in another cloud? The same data can live in both places. You guys make that happen? Is that what you're saying? Exactly. That's a big deal. >> Absolutely. And we guarantee the consistency that a guarantee that you will not get from any other bender. >> So this basically debunks the whole walk in, Yes, that you guys air solution to to essentially relieve this notion of lock and so me as a customer and say, Hey, I'm an Amazon right now. We're all in an Amazon. But, you know, I've got some temptation to goto Azure or Google. Why wouldn't I if I have the ability to make my data consistent, exact. Is that what you're saying? >> That is exactly what I'm saying. You have this ability to experiment with different cloud vendors. You also have the ability to mitigate some of the cost aspect. If you're going to pay for copies in two different geographic locations, you might as well do it on two different cloud vendor see have the richer subset of applications and better availability. >> So for people who say date is a lock inspect for cloud. It's kind of right if unless they use WANdisco because in a sense, and because you know what really moves with it. I mean, your data's Did you stay there? Yeah, that's kind of common sense. It's not so much technical locket, so there's no real technical lockets. More operational lock and correct with data, if you don't wantto. But if you're afraid of lock in, you go with the WANdisco. That's live data. Multi cloud is that >> that was live data multi cloud on. Does this new ability to actually have active data sets that are available in different cloud bender locations? >> Well, that's a killer app right there. How do you feel? You must You must feel pretty good. You know, you and I have talked many times. Yes, but this's like you been waiting for this moment. This is actually really wide here in a k a cloud. I was a big data problem. Which only getting bigger, exactly. Replication is now the transport between clouds for anti lock. And this is the Holy Grail for home when >> it is the Holy Grail for the industrial. We've been talking about it for years now, and we feel completely redeemed. Now we feel that the industry has gotten to the point back. They understand what we've talked about. I feel very excited, the custom attraction we're seeing on watching our customers light of when we describe the attributes we bring, It's >> exciting and just the risk management alone is a hedge. I mean, if I'm a if I'm someone in the cyber security challenges alone on data, you've got data sovereignty, compliance. Never mind the productivity piece of it, which is pretty amazing. So you guys are changing the data equation. >> Indeed, R R No most excited customers are CEOs because mitigating risk from things like cyber security. As you point out, you may have a breach in one cloud vendor. You can turn that off and use your replica in the other cloud vendor side instantly. Those are comfort. You do not get that other solutions. >> So world having a love fest here. I love the whole multi cloud data. No anti lock. And I think that's a killer feature. Think we'll sell that baby? I'm going to say, OK, that's all good, but I'm going to get you on this one. Security. So no one saw security yet. So if you saw that, then you pretty much got it all. So tell me the securities. Just >> so I'll start by saying, right. Our biggest customer base is the financial industry, banking in companies insurance company's health care. There is no industry in the world that's more security conscious than the banking. And does the government the comment? Perhaps I would. I mean, the banks are really security >> conscious, Their money's money, >> money is money. And and they have, ah, judicially responsibility both governments and to their to their customers. So we've catered to these customers for upwards off a decade. Now, every technical decision we make has security. Ask one of the focus items on DH >> years. A good un security. You >> feel's way insecurity when minute comes to date. Yes. >> Encryption. Is that what this is? It's >> encrypted on the wire. We support all on this data at rest encryption schemes. We support all the the the soup and the cloud vendor security mechanisms. We have a cross cloud product, so the security problems are multiplied and we take care of each of those specifically. So you can be confident that your data secure >> and wire speed security, no overhead involved, >> no overhead involved at all. It's not measurable. >> So well, congratulations on where you guys are a lot more work to do. You guys going to staff? So you hiring a lot of people talk about the talent you're hiring real quick because, you know large skin attracting large scale talent is also one indicator. Yeah, the successful opportunity. I see, the more I think the positioning is phenomenal. Congratulations absent about the hiring, >> as you know, as as David mentioned. A few minutes ago, we hired Joel from IBM for our marketing a department. He cmo wonderful. Higher. We've got Ronchi, who's from the University of Denver. I left the head of that computer science department to come work for us. Another amazing guy. Terrific background. We've got shocked me. Who's another column? UT Austin, phD. He's running engineering for us. We're so pleased to be able to hire talent at this level. As as you well know, it's the people who make these jobs interesting and products interesting. We are. So what are >> some of the things that those guys say when they when they get into really exposed. I mean, why would someone with somewhat what would take someone to quit their ten year professor job at a university, which is pretty much retirement to engage in a growing opportunity? What's the What do they say? >> So the single I mean that you'll find in all of this is very complex, unique technology that has bean refined on it's on the verge of exploding toe, probably something ten to one hundred times the size it is today. People see that when dish when we show them the value ofthe what we've got on the market, that we're taking this too. I'm just getting excited. >> Well, congratulations. You guys have certainly worked hard. Has been great to watch the entrepreneurial journey of getting into that growth stream and just the winds that you're back all that hard work into technologies. Phenomenal again. Multi cloud data not worrying about where your data is is going to give people some East and rest in the other rest of night. Well, because that's the number one of the number one was besides security absolutely Jagane Sundar CTO chief technology officer of WANdisco here inside the CUBE in Palo Alto. I'm John Furrier. Thanks for watching.