>> Narrator: From around the globe. It's theCUBE with digital coverage of Exascale Day. Made possible by Hewlett Packard Enterprise. >> Welcome everyone to theCUBE's celebration of Exascale Day. Dr. Mark Fernandez is here. He's the HPC technology officer for the Americas at Hewlett Packard enterprise. And he's a developer of the spaceborne computer, which we're going to talk about today. Mark, welcome. It's great to see you. >> Great to be here. Thanks for having me. >> You're very welcome. So let's start with Exascale Day. It's on 10 18, of course, which is 10 to the power of 18. That's a one followed by 18 zeros. I joke all the time. It takes six commas to write out that number. (Mark laughing) But Mark, why don't we start? What's the significance of that number? >> So it's a very large number. And in general, we've been marking the progress of our computational capabilities in thousands. So exascale is a thousand times faster than where we are today. We're in an era today called the petaflop era which is 10 to the 15th. And prior to that, we were in the teraflop era, which is 10 to the 12th. I can kind of understand a 10 to the 12th and I kind of can discuss that with folks 'cause that's a trillion of something. And we know a lot of things that are in trillions, like our national debt, for example. (Dave laughing) But a billion, billion is an exascale and it will give us a thousand times more computational capability than we have in general today. >> Yeah, so when you think about going from terascale to petascale to exascale I mean, we're not talking about orders of magnitude, we're talking about a much more substantial improvement. And that's part of the reason why it's sort of takes so long to achieve these milestones. I mean, it kind of started back in the sixties and seventies and then... >> Yeah. >> We've been in the petascale now for more than a decade if I think I'm correct. >> Yeah, correct. We got there in 2007. And each of these increments is an extra comma, that's the way to remember it. So we want to add an extra comma and get to the exascale era. So yeah, like you say, we entered the current petaflop scale in 2007. Before that was the terascale, teraflop era and it was in 1997. So it took us 10 years to get that far, but it's taken us, going to take us 13 or 14 years to get to the next one. >> And we say flops, we're talking about floating point operations. And we're talking about the number of calculations that can be done in a second. I mean, talk about not being able to get your head around it, right? Is that's what talking about here? >> Correct scientists, engineers, weather forecasters, others use real numbers and real math. And that's how you want to rank those performance is based upon those real numbers times each other. And so that's why they're floating point numbers. >> When I think about supercomputers, I can't help but remember whom I consider the father of supercomputing Seymour Cray. Cray of course, is a company that Hewlett Packard Enterprise acquired. And he was kind of an eclectic fellow. I mean, maybe that's unfair but he was an interesting dude. But very committed to his goal of really building the world's fastest computers. When you look at back on the industry, how do you think about its developments over the years? >> So one of the events that stands out in my mind is I was working for the Naval Research Lab outside Stennis Space Center in Mississippi. And we were doing weather modeling. And we got a Cray supercomputer. And there was a party when we were able to run a two week prediction in under two weeks. So the scientists and engineers had the math to solve the problem, but the current computers would take longer than just sitting and waiting and looking out the window to see what the weather was like. So when we can make a two week prediction in under two weeks, there was a celebration. And that was in the eighties, early nineties. And so now you see that we get weather predictions in eight hours, four hours and your morning folks will get you down to an hour. >> I mean, if you think about the history of super computing it's really striking to consider the challenges in the efforts as we were just talking about, I mean, decade plus to get to the next level. And you see this coming to fruition now, and we're saying exascale likely 2021. So what are some of the innovations in science, in medicine or other areas you mentioned weather that'll be introduced as exascale computing is ushered in, what should people expect? >> So we kind of alluded to one and weather affects everybody, everywhere. So we can get better weather predictions, which help everybody every morning before you get ready to go to work or travel or et cetera. And again, storm predictions, hurricane predictions, flood predictions, the forest fire predictions, those type things affect everybody, everyday. Those will get improved with exascale. In terms of medicine, we're able to take, excuse me, we're able to take genetic information and attempt to map that to more drugs quicker than we have in the past. So we'll be able to have drug discovery happening much faster with an exascale system out there. And to some extent that's happening now with COVID and all the work that we're doing now. And we realize that we're struggling with these current computers to find these solutions as fast as everyone wants them. And exascale computers will help us get there much faster in the future in terms of medicine. >> Well, and of course, as you apply machine intelligence and AI and machine learning to the applications running on these supercomputers, that just takes it to another level. I mean, people used to joke about you can't predict the weather and clearly we've seen that get much, much better. Now it's going to be interesting to see with climate change. That's another wildcard variable but I'm assuming the scientists are taking that into consideration. I mean, actually been pretty accurate about the impacts of climate change, haven't they? >> Yeah, absolutely. And the climate change models will get better with exascale computers too. And hopefully we'll be able to build a confidence in the public and the politicians in those results with these better, more powerful computers. >> Yeah let's hope so. Now let's talk about the spaceborne computer and your involvement in that project. Your original spaceborne computer it went up on a SpaceX reusable rocket. Destination of course, was the international space station. Okay, so what was the genesis of that project and what was the outcome? So we were approached by a long time customer NASA Ames. And NASA Ames says its mission is to model rocket launches and space missions and return to earth. And they had the foresight to realize that their supercomputers here on earth, could not do that mission when we got to Mars. And so they wanted to plan ahead and they said, "Can you take a small part of our supercomputer today and just prove that it can work in space? And if it can't figure out what we need to do to make it work, et cetera." So that's what we did. We took identical hardware, that's present at NASA Ames. We put it on a SpaceX rocket no special preparations for it in terms of hardware or anything of that sort, no special hardening, because we want to take the latest technology just before we head to Mars with us. I tell people you wouldn't want to get in the rocket headed to Mars with a flip phone. You want to take the latest iPhone, right? And all of the computers on board, current spacecrafts are about the 2007 era that we were talking about, in that era. So we want to take something new with us. We got the spaceone computer on board. It was installed in the ceiling because in space, there's no gravity. And you can put computers in the ceiling. And we immediately made a computer run. And we produced a trillion calculations a second which got us into the teraflop range. The first teraflop in space was pretty exciting. >> Well, that's awesome. I mean, so this is the ultimate example of edge computing. >> Yes. You mentioned you wanted to see if it could work and it sounds like it did. I mean, there was obviously a long elapse time to get it up and running 'cause you have to get it up there. But it sounds like once you did, it was up and running very quickly so it did work. But what were some of the challenges that you encountered maybe some of the learnings in terms of getting it up and running? >> So it's really fascinating. Astronauts are really cool people but they're not computer scientists, right? So they see a cord, they see a place to plug it in, they plug it in and of course we're watching live on the video and you plugged it in the wrong spot. So (laughs) Mr. Astronaut, can we back up and follow the procedure more carefully and get this thing plugged in carefully. They're not computer technicians used to installing a supercomputer. So we were able to get the system packaged for the shake, rattle and roll and G-forces of launch in the SpaceX. We were able to give astronaut instructions on how to install it and get it going. And we were able to operate it here from earth and get some pretty exciting results. >> So our supercomputers are so easy to install even an astronaut can do it. I don't know. >> That's right. (both laughing) Here on earth we have what we call a customer replaceable units. And we had to replace a component. And we looked at our instructions that are tried and true here on earth for average Joe, a customer to do that and realized without gravity, we're going to have to update this procedure. And so we renamed it an astronaut replaceable unit and it worked just fine. >> Yeah, you can't really send an SE out to space to fix it, can you? >> No sir. (Dave laughing) You have to have very careful instructions for these guys but they're great. It worked out wonderfully. >> That's awesome. Let's talk about spaceborne two. Now that's on schedule to go back to the ISS next year. What are you trying to accomplish this time? >> So in retrospect, spaceborne one was a proof of concept. Can we package it up to fit on SpaceX? Can we get the astronauts to install it? And can we operate it from earth? And if so, how long will it last? And do we get the right answers? 100% mission success on that. Now spaceborne two is, we're going to release it to the community of scientists, engineers and space explorers and say, "Hey this thing is rock solid, it's proven. Come use it to improve your edge computing." We'd like to preserve the network downlink bandwidth for all that imagery, all that genetic data, all that other data and process it on the edge as the whole world is moving to now. Don't move the data, let's compute at the edge and that's what we're going to do with spaceborne two. And so what's your expectation for how long the project is going to last? What does success look like in your mind? So spaceborne one was given a one year mission just to see if we could do it but the idea then was planted it's going to take about three years to get to Mars and back. So if you're successful, let's see if this computer can last three years. And so we're going up February 1st, if we go on schedule and we'll be up two to three years and as long as it works, we'll keep computing and computing on the edge. >> That's amazing. I mean, I feel like, when I started the industry, it was almost like there was a renaissance in supercomputing. You certainly had Cray and you had all these other companies, you remember thinking machines and convex spun out tried to do a mini supercomputer. And you had, really a lot of venture capital and then things got quiet for a while. I feel like now with all this big data and AI, we're seeing in all the use cases that you talked about, we're seeing another renaissance in supercomputing. I wonder if you could give us your final thoughts. >> Yeah, absolutely. So we've got the generic like you said, floating point operations. We've now got specialized image processing processors and we have specialized graphics processing units, GPUs. So all of the scientists and engineers are looking at these specialized components and bringing them together to solve their missions at the edge faster than ever before. So there's heterogeneity of computing is coming together to make humanity a better place. And how are you going to celebrate Exascale Day? You got to special cocktail you going to shake up or what are you going to do? It's five o'clock somewhere on 10 18, and I'm a Parrothead fan. So I'll probably have a margarita. There you go all right. Well Mark, thanks so much for sharing your thoughts on Exascale Day. Congratulations on your next project, the spaceborne two. Really appreciate you coming to theCUBE. Thank you very much I've enjoyed it. All right, you're really welcome. And thank you for watching everybody. Keep it right there. This is Dave Vellante for thecUBE. We're celebrating Exascale Day. We'll be right back. (upbeat music)

from around the globe it's thecube with digital coverage of exascale day made possible by hewlett packard enterprise welcome everyone to the cube celebration of exascale day ben bennett is here he's an hpc strategist and evangelist at hewlett-packard enterprise ben welcome good to see you good to see you too son hey well let's evangelize exascale a little bit you know what's exciting you uh in regards to the coming of exoskilled computing um well there's a couple of things really uh for me historically i've worked in super computing for many years and i have seen the coming of several milestones from you know actually i'm old enough to remember gigaflops uh coming through and teraflops and petaflops exascale is has been harder than many of us anticipated many years ago the sheer amount of technology that has been required to deliver machines of this performance has been has been us utterly staggering but the exascale era brings with it real solutions it gives us opportunities to do things that we've not been able to do before if you look at some of the the most powerful computers around today they've they've really helped with um the pandemic kovid but we're still you know orders of magnitude away from being able to design drugs in situ test them in memory and release them to the public you know we still have lots and lots of lab work to do and exascale machines are going to help with that we are going to be able to to do more um which ultimately will will aid humanity and they used to be called the grand challenges and i still think of them as that i still think of these challenges for scientists that exascale class machines will be able to help but also i'm a realist is that in 10 20 30 years time you know i should be able to look back at this hopefully touch wood look back at it and look at much faster machines and say do you remember the days when we thought exascale was faster yeah well you mentioned the pandemic and you know the present united states was tweeting this morning that he was upset that you know the the fda in the u.s is not allowing the the vaccine to proceed as fast as you'd like it in fact it the fda is loosening some of its uh restrictions and i wonder if you know high performance computing in part is helping with the simulations and maybe predicting because a lot of this is about probabilities um and concerns is is is that work that is going on today or are you saying that that exascale actually you know would be what we need to accelerate that what's the role of hpc that you see today in regards to sort of solving for that vaccine and any other sort of pandemic related drugs so so first a disclaimer i am not a geneticist i am not a biochemist um my son is he tries to explain it to me and it tends to go in one ear and out the other um um i just merely build the machines he uses so we're sort of even on that front um if you read if you had read the press there was a lot of people offering up systems and computational resources for scientists a lot of the work that has been done understanding the mechanisms of covid19 um have been you know uncovered by the use of very very powerful computers would exascale have helped well clearly the faster the computers the more simulations we can do i think if you look back historically no vaccine has come to fruition as fast ever under modern rules okay admittedly the first vaccine was you know edward jenner sat quietly um you know smearing a few people and hoping it worked um i think we're slightly beyond that the fda has rules and regulations for a reason and we you don't have to go back far in our history to understand the nature of uh drugs that work for 99 of the population you know and i think exascale widely available exoscale and much faster computers are going to assist with that imagine having a genetic map of very large numbers of people on the earth and being able to test your drug against that breadth of person and you know that 99 of the time it works fine under fda rules you could never sell it you could never do that but if you're confident in your testing if you can demonstrate that you can keep the one percent away for whom that drug doesn't work bingo you now have a drug for the majority of the people and so many drugs that have so many benefits are not released and drugs are expensive because they fail at the last few moments you know the more testing you can do the more testing in memory the better it's going to be for everybody uh personally are we at a point where we still need human trials yes do we still need due diligence yes um we're not there yet exascale is you know it's coming it's not there yet yeah well to your point the faster the computer the more simulations and the higher the the chance that we're actually going to going to going to get it right and maybe compress that time to market but talk about some of the problems that you're working on uh and and the challenges for you know for example with the uk government and maybe maybe others that you can you can share with us help us understand kind of what you're hoping to accomplish so um within the united kingdom there was a report published um for the um for the uk research institute i think it's the uk research institute it might be epsrc however it's the body of people responsible for funding um science and there was a case a science case done for exascale i'm not a scientist um a lot of the work that was in this documentation said that a number of things that can be done today aren't good enough that we need to look further out we need to look at machines that will do much more there's been a program funded called asimov and this is a sort of a commercial problem that the uk government is working with rolls royce and they're trying to research how you build a full engine model and by full engine model i mean one that takes into account both the flow of gases through it and how those flow of gases and temperatures change the physical dynamics of the engine and of course as you change the physical dynamics of the engine you change the flow so you need a closely coupled model as air travel becomes more and more under the microscope we need to make sure that the air travel we do is as efficient as possible and currently there aren't supercomputers that have the performance one of the things i'm going to be doing as part of this sequence of conversations is i'm going to be having an in detailed uh sorry an in-depth but it will be very detailed an in-depth conversation with professor mark parsons from the edinburgh parallel computing center he's the director there and the dean of research at edinburgh university and i'm going to be talking to him about the azimoth program and and mark's experience as the person responsible for looking at exascale within the uk to try and determine what are the sort of science problems that we can solve as we move into the exoscale era and what that means for humanity what are the benefits for humans yeah and that's what i wanted to ask you about the the rolls-royce example that you gave it wasn't i if i understood it wasn't so much safety as it was you said efficiency and so that's that's what fuel consumption um it's it's partly fuel consumption it is of course safety there is a um there is a very specific test called an extreme event or the fan blade off what happens is they build an engine and they put it in a cowling and then they run the engine at full speed and then they literally explode uh they fire off a little explosive and they fire a fan belt uh a fan blade off to make sure that it doesn't go through the cowling and the reason they do that is there has been in the past uh a uh a failure of a fan blade and it came through the cowling and came into the aircraft depressurized the aircraft i think somebody was killed as a result of that and the aircraft went down i don't think it was a total loss one death being one too many but as a result you now have to build a jet engine instrument it balance the blades put an explosive in it and then blow the fan blade off now you only really want to do that once it's like car crash testing you want to build a model of the car you want to demonstrate with the dummy that it is safe you don't want to have to build lots of cars and keep going back to the drawing board so you do it in computers memory right we're okay with cars we have computational power to resolve to the level to determine whether or not the accident would hurt a human being still a long way to go to make them more efficient uh new materials how you can get away with lighter structures but we haven't got there with aircraft yet i mean we can build a simulation and we can do that and we can be pretty sure we're right um we still need to build an engine which costs in excess of 10 million dollars and blow the fan blade off it so okay so you're talking about some pretty complex simulations obviously what are some of the the barriers and and the breakthroughs that are kind of required you know to to do some of these things that you're talking about that exascale is going to enable i mean presumably there are obviously technical barriers but maybe you can shed some light on that well some of them are very prosaic so for example power exoscale machines consume a lot of power um so you have to be able to design systems that consume less power and that goes into making sure they're cooled efficiently if you use water can you reuse the water i mean the if you take a laptop and sit it on your lap and you type away for four hours you'll notice it gets quite warm um an exascale computer is going to generate a lot more heat several megawatts actually um and it sounds prosaic but it's actually very important to people you've got to make sure that the systems can be cooled and that we can power them yeah so there's that another issue is the software the software models how do you take a software model and distribute the data over many tens of thousands of nodes how do you do that efficiently if you look at you know gigaflop machines they had hundreds of nodes and each node had effectively a processor a core a thread of application we're looking at many many tens of thousands of nodes cores parallel threads running how do you make that efficient so is the software ready i think the majority of people will tell you that it's the software that's the problem not the hardware of course my friends in hardware would tell you ah software is easy it's the hardware that's the problem i think for the universities and the users the challenge is going to be the software i think um it's going to have to evolve you you're just you want to look at your machine and you just want to be able to dump work onto it easily we're not there yet not by a long stretch of the imagination yeah consequently you know we one of the things that we're doing is that we have a lot of centers of excellence is we will provide well i hate say the word provide we we sell super computers and once the machine has gone in we work very closely with the establishments create centers of excellence to get the best out of the machines to improve the software um and if a machine's expensive you want to get the most out of it that you can you don't just want to run a synthetic benchmark and say look i'm the fastest supercomputer on the planet you know your users who want access to it are the people that really decide how useful it is and the work they get out of it yeah the economics is definitely a factor in fact the fastest supercomputer in the planet but you can't if you can't afford to use it what good is it uh you mentioned power uh and then the flip side of that coin is of course cooling you can reduce the power consumption but but how challenging is it to cool these systems um it's an engineering problem yeah we we have you know uh data centers in iceland where it gets um you know it doesn't get too warm we have a big air cooled data center in in the united kingdom where it never gets above 30 degrees centigrade so if you put in water at 40 degrees centigrade and it comes out at 50 degrees centigrade you can cool it by just pumping it round the air you know just putting it outside the building because the building will you know never gets above 30 so it'll easily drop it back to 40 to enable you to put it back into the machine um right other ways to do it um you know is to take the heat and use it commercially there's a there's a lovely story of they take the hot water out of the supercomputer in the nordics um and then they pump it into a brewery to keep the mash tuns warm you know that's that's the sort of engineering i can get behind yeah indeed that's a great application talk a little bit more about your conversation with professor parsons maybe we could double click into that what are some of the things that you're going to you're going to probe there what are you hoping to learn so i think some of the things that that are going to be interesting to uncover is just the breadth of science that can be uh that could take advantage of exascale you know there are there are many things going on that uh that people hear about you know we people are interested in um you know the nobel prize they might have no idea what it means but the nobel prize for physics was awarded um to do with research into black holes you know fascinating and truly insightful physics um could it benefit from exascale i have no idea uh i i really don't um you know one of the most profound pieces of knowledge in in the last few hundred years has been the theory of relativity you know an austrian patent clerk wrote e equals m c squared on the back of an envelope and and voila i i don't believe any form of exascale computing would have helped him get there any faster right that's maybe flippant but i think the point is is that there are areas in terms of weather prediction climate prediction drug discovery um material knowledge engineering uh problems that are going to be unlocked with the use of exascale class systems we are going to be able to provide more tools more insight [Music] and that's the purpose of computing you know it's not that it's not the data that that comes out and it's the insight we get from it yeah i often say data is plentiful insights are not um ben you're a bit of an industry historian so i've got to ask you you mentioned you mentioned mentioned gigaflop gigaflops before which i think goes back to the early 1970s uh but the history actually the 80s is it the 80s okay well the history of computing goes back even before that you know yes i thought i thought seymour cray was you know kind of father of super computing but perhaps you have another point of view as to the origination of high performance computing [Music] oh yes this is um this is this is one for all my colleagues globally um you know arguably he says getting ready to be attacked from all sides arguably you know um computing uh the parallel work and the research done during the war by alan turing is the father of high performance computing i think one of the problems we have is that so much of that work was classified so much of that work was kept away from commercial people that commercial computing evolved without that knowledge i uh i have done in in in a previous life i have done some work for the british science museum and i have had the great pleasure in walking through the the british science museum archives um to look at how computing has evolved from things like the the pascaline from blaise pascal you know napier's bones the babbage's machines uh to to look all the way through the analog machines you know what conrad zeus was doing on a desktop um i think i think what's important is it doesn't matter where you are is that it is the problem that drives the technology and it's having the problems that requires the you know the human race to look at solutions and be these kicks started by you know the terrible problem that the us has with its nuclear stockpile stewardship now you've invented them how do you keep them safe originally done through the ascii program that's driven a lot of computational advances ultimately it's our quest for knowledge that drives these machines and i think as long as we are interested as long as we want to find things out there will always be advances in computing to meet that need yeah and you know it was a great conversation uh you're a brilliant guest i i love this this this talk and uh and of course as the saying goes success has many fathers so there's probably a few polish mathematicians that would stake a claim in the uh the original enigma project as well i think i think they drove the algorithm i think the problem is is that the work of tommy flowers is the person who took the algorithms and the work that um that was being done and actually had to build the poor machine he's the guy that actually had to sit there and go how do i turn this into a machine that does that and and so you know people always remember touring very few people remember tommy flowers who actually had to turn the great work um into a working machine yeah super computer team sport well ben it's great to have you on thanks so much for your perspectives best of luck with your conversation with professor parsons we'll be looking forward to that and uh and thanks so much for coming on thecube a complete pleasure thank you and thank you everybody for watching this is dave vellante we're celebrating exascale day you're watching the cube [Music]

>> From around the globe, it's theCUBE, with digital coverage of Exascale Day. Made possible by Hewlett Packard Enterprise. >> Hey, welcome back everybody Jeff Frick here with theCUBE. We are celebrating Exascale Day. 10, 18, I think it's the second year of celebrating Exascale Day, and we're really excited to have our next guest and talk about kind of what this type of compute scale enables, and really look a little bit further down the road at some big issues, big problems and big opportunities that this is going to open up. And I'm really excited to get in this conversation with our next guest. He is Bryan Pijanowski the Professor of Landscape and Soundscape Ecology at Purdue University. Bryan, great to meet you. >> Great to be here. >> So, in getting ready for this conversation, I just watched your TED Talk, and I just loved one of the quotes. I actually got one of quote from it that's basically saying you are exploring the world through sound. I just would love to get a little deeper perspective on that, because that's such a unique way to think about things and you really dig into it and explain why this is such an important way to enjoy the world, to absorb the world and think about the world. >> Yeah, that's right Jeff. So the way I see it, sound is kind of like a universal variable. It exists all around us. And you can't even find a place on earth where there's no sound, where it's completely silent. Sound is a signal of something that's happening. And we can use that information in ways to allow us to understand the earth. Just thinking about all the different kinds of sounds that exist around us on a daily basis. I hear the birds, I hear the insects, but there's just a lot more than that. It's mammals and some cases, a lot of reptiles. And then when you begin thinking outside the biological system, you begin to hear rain, wind, thunder. And then there's the sounds that we make, sounds of traffic, the sounds of church bells. All of this is information, some of it's symbolic, some of it's telling me something about change. As an ecologist that's what I'm interested in, how is the earth changing? >> That's great and then you guys set up at Purdue, the Purdue Center for Global Soundscapes. Tell us a little bit about the mission and some of the work that you guys do. >> Well, our mission is really to use sound as a lens to study the earth, but to capture it in ways that are meaningful and to bring that back to the public to tell them a story about how the earth kind of exists. There's an incredible awe of nature that we all experience when we go out and listen into to the wild spaces of the earth. I've gone to the Eastern Steppes of Mongolian, I've climbed towers in the Paleotropics of Borneo and listened at night. And ask the question, how are these sounds different? And what is a grassland really supposed to sound like, without humans around? So we use that information and bring it back and analyze it as a means to understand how the earth is changing and really what the biological community is all about, and how things like climate change are altering our spaces, our wild spaces. I'm also interested in the role that people play and producing sound and also using sound. So getting back to Mongolia, we have a new NSF funded project where we're going to be studying herders and the ways in which they use sonic practices. They use a lot of sounds as information sources about how the environment is changing, but also how they relate back to place and to heritage a special sounds that resonate, the sounds of a river, for example, are the resonance patterns that they tune their throat to that pay homage to their parents that were born at the side of that river. There's these special connections that people have with place through sound. And so that's another thing that we're trying to do. In really simple terms, I want to go out and, what I call it sounds rather simple, record the earth-- >> Right. >> What does that mean? I want to go to every major biome and conduct a research study there. I want to know what does a grassland sound like? What is a coral reef sound like? A kelp forest and the oceans, a desert, and then capture that as baseline and use that information-- >> Yeah. >> For scientific purposes >> Now, there's so much to unpack there Bryan. First off is just kind of the foundational role that sound plays in our lives that you've outlined in great detail and you talked about it's the first sense that's really activated as we get consciousness, even before we're born right? We hear the sounds of our mother's heartbeat and her voice. And even the last sense that goes at the end a lot of times, in this really intimate relationship, as you just said, that the sounds represent in terms of our history. We don't have to look any further than a favorite song that can instantly transport you, almost like a time machine to a particular place in time. Very, very cool. Now, it's really interesting that what you're doing now is taking advantage of new technology and just kind of a new angle to capture sound in a way that we haven't done before. I think you said you have sound listening devices oftentimes in a single location for a year. You're not only capturing sound, the right sound is changes in air pressure, so that you're getting changes in air pressure, you're getting vibration, which is kind of a whole different level of data. And then to be able to collect that for a whole year and then start to try to figure out a baseline which is pretty simple to understand, but you're talking about this chorus. I love your phrase, a chorus, because that sound is made up of a bunch of individual inputs. And now trying to kind of go under the covers to figure out what is that baseline actually composed of. And you talk about a bunch of really interesting particular animals and species that combine to create this chorus that now you know is a baseline. How did you use to do that before? I think it's funny one of your research papers, you reach out to the great bird followers and bird listeners, 'cause as you said, that's the easiest way or the most prolific way for people to identify birds. So please help us in a crowdsource way try to identify all the pieces that make this beautiful chorus, that is the soundscape for a particular area. >> Right, yeah, that's right. It really does take a team of scientists and engineers and even folks in the social sciences and the humanities to really begin to put all of these pieces together. Experts in many fields are extremely valuable. They've got great ears because that's the tools that they use to go out and identify birds or insects or amphibians. What we don't have are generalists that go out and can tell you what everything sounds like. And I'll tell you that will probably never ever happen. That's just way too much, we have millions of species that exist on this planet. And we just don't have a specific catalog of what everything sounds like, it's just not possible or doable. So I need to go out and discover and bring those discoveries back that help us to understand nature and understand how the earth is changing. I can't wait for us to eventually develop that catalog. So we're trying to develop techniques and tools and approaches that allow us to develop this electronic catalog. Like you're saying this chorus, and it doesn't necessarily have to be a species specific chorus, it can be a chorus of all these different kind of sounds that we think relate back to this kind of animal or that kind of animal based upon the animals instrument-- >> Right, great. >> And this is the sound. >> Now again, you know, keep it to the exascale theme, right? You're collecting a lot of data and you mentioned in one of the pieces I've dug up, that your longest study in a single location is 17 years. You've got over 4 million recordings. And I think you said over 230 years if you wanted to listen to them all back to back. I mean, this is a huge, a big data problem in terms of the massive amount of data that you have and need to run through an analysis. >> Yeah, that's right. We're collecting 48,000 data points per second. So that's 48 kilohertz. And then so you multiply everything and then you have a sense of how many data points you actually have to put them all together. When you're listening to a sound file over 10 minutes, you have hundreds of sounds that exist in them. Oftentimes you just don't know what they are, but you can more or less put some kind of measure on all of them and then begin to summarize them over space and time and try to understand it from a perspective of really science. >> Right, right. And then I just love to get your take as you progress down this kind of identification road, we're all very familiar with copyright infringement hits on YouTube or social media or whatever, when it picks up on some sound and the technology is actually really sophisticated to pick up some of those sound signatures. But to your point, it's a lot easier to compare against the known and to search for that known. Then when you've got this kind of undefined chorus that said we do know that there can be great analysis done that we've seen AI and ML applied, especially in the surveillance side on the video-- >> Right. >> With video that it can actually do a lot of computation and a lot of extracting signal from the noise, if you will. As you look down the road on the compute side for the algorithms that you guys are trying to build with the human input of people that know what you're listening to, what kind of opportunities do you see and where are we on that journey where you can get more leverage out of some of these technology tools? >> Well, I think what we're doing right now is developing the methodological needs, kind of describe what it is we need to move into that new space, which is going to require these computational, that computational infrastructure. So, for example, we have a study right now where we're trying to identify certain kinds of mosquitoes (chuckling) a vector-borne mosquitoes, and our estimates is that we need about maybe 900 to 1200 specific recordings per species to be able to put it into something like a convolutional neural network to be able to extract out the information, and look at the patterns and data, to be able to say indeed this is the species that we're interested in. So what we're going to need and in the future here is really a lot of information that allow us to kind of train these neural networks and help us identify what's in the sound files. As you can imagine the computational infrastructure needed to do that for data storage and CPU, GPU is going to be truly amazing. >> Right, right. So I want to get your take on another topic. And again the basis of your research is really all bound around the biodiversity crisis right? That's from the kind of-- >> Yeah. >> The thing that's started it and now you're using sound as a way to measure baseline and talk about loss of species, reduced abundancies and rampant expansion of invasive species as part of your report. But I'd love to get your take on cities. And how do you think cities fit the future? Clearly, it's an efficient way to get a lot of people together. There's a huge migration of people-- >> Right. >> To cities, but one of your themes in your Ted Talk is reconnecting with nature-- >> Yeah. >> Because we're in cities, but there's this paradox right? Because you don't want people living in nature can be a little bit disruptive. So is it better to kind of get them all in a tip of a peninsula in San Francisco or-- >> Yeah. >> But then do they lose that connection that's so important. >> Yeah. >> I just love to get your take on cities and the impacts that they're have on your core research. >> Yeah, I mean, it truly is a paradox as you just described it. We're living in a concrete jungle surrounded by not a lot of nature, really, honestly, occasional bird species that tend to be fairly limited, selected for limited environments. So many people just don't get out into the wild. But visiting national parks certainly is one of those kinds of experience that people oftentimes have. But I'll just say that it's getting out there and truly listening and feeling this emotional feeling, psychological feeling that wraps around you, it's a solitude. It's just you and nature and there's just no one around. >> Right. >> And that's when it really truly sinks in, that you're a part of this place, this marvelous place called earth. And so there are very few people that have had that experience. And so as I've gone to some of these places, I say to myself I need to bring this back. I need to tell the story, tell the story of the awe of nature, because it truly is an amazing place. Even if you just close your eyes and listen. >> Right, right. >> And it, the dawn chorus in the morning in every place tells me so much about that place. It tells me about all the animals that exist there. The nighttime tells me so much too. As a scientist that's spent most of his career kind of going out and working during the day, there's so much happening at night. Matter of fact-- >> Right. >> There's more sounds at night than there were during the day. So there is a need for us to experience nature and we don't do that. And we're not aware of these crises that are happening all over the planet. I do go to places and I listen, and I can tell you I'm listening for things that I think should be there. You can listen and you can hear the gaps, the gaps and that in that chorus, and you think what should be there-- >> Right. >> And then why isn't it there? And that's where I really want to be able to dig deep into my sound files and start to explore that more fully. >> It's great, it's great, I mean, I just love the whole concept of, and you identified it in the moment you're in the tent, the thunderstorm came by, it's really just kind of changing your lens. It's really twisting your lens, changing your focus, because that sound is there, right? It's been there all along, it's just, do you tune it in or do you tune it out? Do you pay attention? Do not pay attention is an active process or a passive process and like-- >> Right. >> I love that perspective. And I want to shift gears a little bit, 'cause another big environmental thing, and you mentioned it quite frequently is feeding the world's growing population and feeding it-- >> Yeah. >> In an efficient way. And anytime you see kind of factory farming applied to a lot of things you wonder is it sustainable, and then all the issues that come from kind of single output production whether that's pigs or coffee or whatever and the susceptibility to disease and this and that. So I wonder if you could share your thoughts on, based on your research, what needs to change to successfully and without too much destruction feed this ever increasing population? >> Yeah, I mean, that's one of the grand challenges. I mean, society is facing so many at the moment. In the next 20 years or so, 30 years, we're going to add another 2 billion people to the planet, and how do we feed all of them? How do we feed them well and equitably across the globe? I don't know how to do that. But I'll tell you that our crops and the ecosystem that supports the food production needs the animals and the trees and the microbes for the ecosystem to function. We have many of our crops that are pollinated by birds and insects and other animals, seeds need to be dispersed. And so we need the rest of life to exist and thrive for us to thrive too. It's not an either, it's not them or us, it has to be all of us together on this planet working together. We have to find solutions. And again, it's me going out to some of these places and bringing it back and saying, you have to listen, you have to listen to these places-- >> Right. >> They're truly a marvelous. >> So I know most of your listening devices are in remote areas and not necessarily in urban areas, but I'm curious, do you have any in urban areas? And if so, how has that signature changed since COVID? I just got to ask, (Bryan chuckling) because we went to this-- >> Yeah. >> Light switch moment in the middle of March, human activity slowed down-- >> Yeah. >> In a way that no one could have forecast ever on a single event, globally which is just fascinating. And you think of the amount of airplanes that were not flying and trains that we're not moving and people not moving. Did you have any any data or have you been able to collect data or see data as the impact of that? Not only directly in wherever the sensors are, but a kind of a second order impact because of the lack of pollution and the other kind of human activity that just went down. I mean, certainly a lot of memes (Bryan chuckling) on social media of all the animals-- >> Yeah. >> Come back into the city. But I'm just curious if you have any data in the observation? >> Yeah, we're part of actually a global study, there's couple of hundred of us that are contributing our data to what we call the Silent Cities project. It's being coordinated out of Europe right now. So we placed our sensors out in different areas, actually around West Lafayette area here in Indiana, near road crossings and that sort of thing to be able to kind of capture that information. We have had in this area here now, the 17 year study. So we do have studies that get into areas that tend to be fairly urban. So we do have a lot of information. I tell you, I don't need my sensors to tell me something that I already know and you suspect is true. Our cities were quiet, much quieter during the COVID situation. And it's continued to kind of get a little bit louder, as we've kind of released some of the policies that put us into our homes. And so yes, there is a major change. Now there have been a couple of studies that just come out that are pretty interesting. One, which was in San Francisco looking at the white-crowned sparrow. And they looked at historical data that went back something like 20 years. And they found that the birds in the cities were singing a much softer, 30% softer. >> Really? >> And they, yeah, and they would lower their frequencies. So the way sound works is that if you lower your frequencies that sound can travel farther. And so the males can now hear themselves twice as far just due to the fact that our cities are quieter. So it does have an impact on animals, truly it does. There was some studies back in 2001, during  the September, the 9/11 crisis as well, where people are going out and kind of looking at data, acoustic data, and discovering that things were much quieter. I'd be very interested to look at some of the data we have in our oceans, to what extent are oceans quieter. Our oceans sadly are the loudest part of this planet. It's really noisy, sound travels, five times farther. Generally the noise is lower frequencies, and we have lots of ships that are all over the planet and in our oceans. So I'd really be interested in those kinds of studies as well, to what extent is it impacting and helping our friends in the oceans. >> Right, right, well, I was just going to ask you that question because I think a lot of people clearly understand sound in the air that surrounds us, but you talk a lot about sound in ocean, and sound as an indicator of ocean health, and again, this concept of a chorus. And I think everybody's probably familiar with the sounds of the humpback whale right? He got very popular and we've all seen and heard that. But you're doing a lot of research, as you said, in oceans and in water. And I wonder if you can, again, kind of provide a little bit more color around that, because I don't think you people, maybe we're just not that tuned into it, think of the ocean or water as a rich sound environment especially to the degree as you're talking about where you can actually start to really understand what's going on. >> Yeah, I mean, some of us think that sound in the oceans is probably more important to animals than on land, on the terrestrial side. Sound helps animals to navigate through complex waterways and find food resources. You can only use site so far underwater especially when it gets to be kind of dark, once you get down to certain levels. So there many of us think that sound is probably going to be an important component to measuring the status of health in our oceans. >> It's great. Well, Bryan, I really enjoyed this conversation. I've really enjoyed your Ted Talk, and now I've got a bunch of research papers I want to dig into a little bit more as well. >> Okay.(chuckling) >> It's a fascinating topic, but I think the most important thing that you talked about extensively in your Ted Talk is really just taking a minute to take a step back from the individual perspective, appreciate what's around us, hear, that information and I think there's a real direct correlation to the power of exascale, to the power of hearing this data, processing this data, and putting intelligence on that data, understanding that data in a good way, in a positive way, in a delightful way, spiritual way, even that we couldn't do before, or we just weren't paying attention like with what you know is on your phone please-- >> Yeah, really. >> It's all around you. It's been there a whole time. >> Yeah. (both chuckling) >> Yeah, Jeff, I really encourage your viewers to count it, just go out and listen. As we say, go out and listen and join the mission. >> I love it, and you can get started by going to the Center for Global Soundscapes and you have a beautiful landscape. I had it going earlier this morning while I was digging through some of the research of Bryan. (Bryan chuckling) Thank you very much (Bryan murmurs) and really enjoyed the conversation best to you-- >> Okay. >> And your team and your continued success. >> Alright, thank you. >> Alright, thank you. All right, he's Bryan-- >> Goodbye. >> I'm Jeff, you're watching theCUBE. (Bryan chuckling) for continuing coverage of Exascale Day. Thanks for watching. We'll see you next time. (calm ambient music)

>> Narrator: From around the globe, it's theCUBE with digital coverage of Exascale day, made possible by Hewlett Packard Enterprise. >> Hey, welcome back Jeff Frick here with the cube come due from our Palo Alto studios, for their ongoing coverage in the celebration of Exascale day 10 to the 18th on October 18th, 10 with 18 zeros, it's all about big powerful giant computing and computing resources and computing power. And we're excited to invite back our next guest she's been on before. She's Dr. Arti Garg, head of advanced AI solutions and technologies for HPE. Arti great to see you again. >> Great to see you. >> Absolutely. So let's jump into before we get into Exascale day I was just looking at your LinkedIn profile. It's such a very interesting career. You've done time at Lawrence Livermore, You've done time in the federal government, You've done time at GE and industry, I just love if you can share a little bit of your perspective going from hardcore academia to, kind of some government positions, then into industry as a data scientist, and now with originally Cray and now HPE looking at it really from more of a vendor side. >> Yeah. So I think in some ways, I think I'm like a lot of people who've had the title of data scientists somewhere in their history where there's no single path, to really working in this industry. I come from a scientific background. I have a PhD in physics, So that's where I started working with large data sets. I think of myself as a data scientist before the term data scientist was a term. And I think it's an advantage, to be able to have seen this explosion of interest in leveraging data to gain insights, whether that be into the structure of the galaxy, which is what I used to look at, or whether that be into maybe new types of materials that could advance our ability to build lightweight cars or safety gear. It's allows you to take a perspective to not only understand what the technical challenges are, but what also the implementation challenges are, and why it can be hard to use data to solve problems. >> Well, I'd just love to get your, again your perspective cause you are into data, you chose that as your profession, and you probably run with a whole lot of people, that are also like-minded in terms of data. As an industry and as a society, we're trying to get people to do a better job of making database decisions and getting away from their gut and actually using data. I wonder if you can talk about the challenges of working with people who don't come from such an intense data background to get them to basically, I don't know if it's understand the value of more of a data kind decision making process or board just it's worth the effort, cause it's not easy to get the data and cleanse the data, and trust the data and get the right context, working with people that don't come from that background. And aren't so entrenched in that point of view, what surprises you? How do you help them? What can you share in terms of helping everybody get to be a more data centric decision maker? >> So I would actually rephrase the question a little bit Jeff, and say that actually I think people have always made data driven decisions. It's just that in the past we maybe had less data available to us or the quality of it was not as good. And so as a result most organizations have developed organize themselves to make decisions, to run their processes based on a much smaller and more refined set of information, than is currently available both given our ability to generate lots of data, through software and sensors, our ability to store that data. And then our ability to run a lot of computing cycles and a lot of advanced math against that data, to learn things that maybe in the past took, hundreds of years of experiments in scientists to understand. And so before I jumped into, how do you overcome that barrier? Just I'll use an example because you mentioned, I used to work in industry I used to work at GE. And one of the things that I often joked about, is the number of times I discovered Bernoulli's principle, in data coming off a GE jet engines you could do that overnight processing these large data but of course historically that took hundreds of years, to really understand these physical principles. And so I think when it comes to how do we bridge the gap between people who are adapt at processing large amounts of data, and running algorithms to pull insights out? I think it's both sides. I think it's those of us who are coming from the technical background, really understanding the way decisions are currently made, the way process and operations currently work at an organization. And understanding why those things are the way they are maybe their security or compliance or accountability concerns, that a new algorithm can't just replace those. And so I think it's on our end, really trying to understand, and make sure that whatever new approaches we're bringing address those concerns. And I think for folks who aren't necessarily coming from a large data set, and analytical background and when I say analytical, I mean in the data science sense, not in the sense of thinking about things in an abstract way to really recognize that these are just tools, that can enhance what they're doing, and they don't necessarily need to be frightening because I think that people who have been say operating electric grids for a long time, or fixing aircraft engines, they have a lot of expertise and a lot of understanding, and that's really important to making any kind of AI driven solution work. >> That's great insight but that but I do think one thing that's changed you come from a world where you had big data sets, so you kind of have a big data set point of view, where I think for a lot of decision makers they didn't have that data before. So we won't go through all the up until the right explosions of data, and obviously we're talking about Exascale day, but I think for a lot of processes now, the amount of data that they can bring to bear, is so dwarfs what they had in the past that before they even consider how to use it they still have to contextualize it, and they have to manage it and they have to organize it and there's data silos. So there's all this kind of nasty processes stuff, that's in the way some would argue has been kind of a real problem with the promise of BI, and does decision support tools. So as you look at at this new stuff and these new datasets, what are some of the people in process challenges beyond the obvious things that we can think about, which are the technical challenges? >> So I think that you've really hit on, something I talk about sometimes it was kind of a data deluge that we experienced these days, and the notion of feeling like you're drowning in information but really lacking any kind of insight. And one of the things that I like to think about, is to actually step back from the data questions the infrastructure questions, sort of all of these technical questions that can seem very challenging to navigate. And first ask ourselves, what problems am I trying to solve? It's really no different than any other type of decision you might make in an organization to say like, what are my biggest pain points? What keeps me up at night? or what would just transform the way my business works? And those are the problems worth solving. And then the next question becomes, if I had more data if I had a better understanding of something about my business or about my customers or about the world in which we all operate, would that really move the needle for me? And if the answer is yes, then that starts to give you a picture of what you might be able to do with AI, and it starts to tell you which of those data management challenges, whether they be cleaning the data, whether it be organizing the data, what it, whether it be building models on the data are worth solving because you're right, those are going to be a time intensive, labor intensive, highly iterative efforts. But if you know why you're doing it, then you will have a better understanding of why it's worth the effort. And also which shortcuts you can take which ones you can't, because often in order to sort of see the end state you might want to do a really quick experiment or prototype. And so you want to know what matters and what doesn't at least to that. Is this going to work at all time. >> So you're not buying the age old adage that you just throw a bunch of data in a data Lake and the answers will just spring up, just come right back out of the wall. I mean, you bring up such a good point, It's all about asking the right questions and thinking about asking questions. So again, when you talk to people, about helping them think about the questions, cause then you've got to shape the data to the question. And then you've got to start to build the algorithm, to kind of answer that question. How should people think when they're actually building algorithm and training algorithms, what are some of the typical kind of pitfalls that a lot of people fall in, haven't really thought about it before and how should people frame this process? Cause it's not simple and it's not easy and you really don't know that you have the answer, until you run multiple iterations and compare it against some other type of reference? >> Well, one of the things that I like to think about just so that you're sort of thinking about, all the challenges you're going to face up front, you don't necessarily need to solve all of these problems at the outset. But I think it's important to identify them, is I like to think about AI solutions as, they get deployed being part of a kind of workflow, and the workflow has multiple stages associated with it. The first stage being generating your data, and then starting to prepare and explore your data and then building models for your data. But sometimes I think where we don't always think about it is the next two phases, which is deploying whatever model or AI solution you've developed. And what will that really take especially in the ecosystem where it's going to live. If is it going to live in a secure and compliant ecosystem? Is it actually going to live in an outdoor ecosystem? We're seeing more applications on the edge, and then finally who's going to use it and how are they going to drive value from it? Because it could be that your AI solution doesn't work cause you don't have the right dashboard, that highlights and visualizes the data for the decision maker who will benefit from it. So I think it's important to sort of think through all of these stages upfront, and think through maybe what some of the biggest challenges you might encounter at the Mar, so that you're prepared when you meet them, and you can kind of refine and iterate along the way and even upfront tweak the question you're asking. >> That's great. So I want to get your take on we're celebrating Exascale day which is something very specific on 1018, share your thoughts on Exascale day specifically, but more generally I think just in terms of being a data scientist and suddenly having, all this massive compute power. At your disposal yoy're been around for a while. So you've seen the development of the cloud, these huge data sets and really the ability to, put so much compute horsepower against the problems as, networking and storage and compute, just asymptotically approach zero, I mean for as a data scientist you got to be pretty excited about kind of new mysteries, new adventures, new places to go, that we just you just couldn't do it 10 years ago five years ago, 15 years ago. >> Yeah I think that it's, it'll--only time will tell exactly all of the things that we'll be able to unlock, from these new sort of massive computing capabilities that we're going to have. But a couple of things that I'm very excited about, are that in addition to sort of this explosion or these very large investments in large supercomputers Exascale super computers, we're also seeing actually investment in these other types of scientific instruments that when I say scientific it's not just academic research, it's driving pharmaceutical drug discovery because we're talking about these, what they call light sources which shoot x-rays at molecules, and allow you to really understand the structure of the molecules. What Exascale allows you to do is, historically it's been that you would go take your molecule to one of these light sources and you shoot your, x-rays edit and you would generate just masses and masses of data, terabytes of data it was each shot. And being able to then understand, what you were looking at was a long process, getting computing time and analyzing the data. We're on the precipice of being able to do that, if not in real time much closer to real time. And I don't really know what happens if instead of coming up with a few molecules, taking them, studying them, and then saying maybe I need to do something different. I can do it while I'm still running my instrument. And I think that it's very exciting, from the perspective of someone who's got a scientific background who likes using large data sets. There's just a lot of possibility of what Exascale computing allows us to do in from the standpoint of I don't have to wait to get results, and I can either stimulate much bigger say galaxies, and really compare that to my data or galaxies or universes, if you're an astrophysicist or I can simulate, much smaller finer details of a hypothetical molecule and use that to predict what might be possible, from a materials or drug perspective, just to name two applications that I think Exascale could really drive. >> That's really great feedback just to shorten that compute loop. We had an interview earlier in some was talking about when the, biggest workload you had to worry about was the end of the month when you're running your financial, And I was like, why wouldn't that be nice to be the biggest job that we have to worry about? But now I think we saw some of this at animation, in the movie business when you know the rendering for whether it's a full animation movie, or just something that's a heavy duty three effects. When you can get those dailies back to the, to the artist as you said while you're still working, or closer to when you're working versus having this, huge kind of compute delay, it just changes the workflow dramatically and the pace of change and the pace of output. Because you're not context switching as much and you can really get back into it. That's a super point. I want to shift gears a little bit, and talk about explainable AI. So this is a concept that a lot of people hopefully are familiar with. So AI you build the algorithm it's in a box, it runs and it kicks out an answer. And one of the things that people talk about, is we should be able to go in and pull that algorithm apart to know, why it came out with the answer that it did. To me this just sounds really really hard because it's smart people like you, that are writing the algorithms the inputs and the and the data that feeds that thing, are super complex. The math behind it is very complex. And we know that the AI trains and can change over time as you you train the algorithm it gets more data, it adjusts itself. So it's explainable AI even possible? Is it possible at some degree? Because I do think it's important. And my next question is going to be about ethics, to know why something came out. And the other piece that becomes so much more important, is as we use that output not only to drive, human based decision that needs some more information, but increasingly moving it over to automation. So now you really want to know why did it do what it did explainable AI? Share your thoughts. >> It's a great question. And it's obviously a question that's on a lot of people's mind these days. I'm actually going to revert back to what I said earlier, when I talked about Bernoulli's principle, and just the ability sometimes when you do throw an algorithm at data, it might come the first thing it will find is probably some known law of physics. And so I think that really thinking about what do we mean by explainable AI, also requires us to think about what do we mean by AI? These days AI is often used anonymously with deep learning which is a particular type of algorithm that is not very analytical at its core. And what I mean by that is, other types of statistical machine learning models, have some underlying theory of what the population of data that you're studying. And whereas deep learning doesn't, it kind of just learns whatever pattern is sitting in front of it. And so there is a sense in which if you look at other types of algorithms, they are inherently explainable because you're choosing your algorithm based on what you think the is the sort of ground truth, about the population you're studying. And so I think we going to get to explainable deep learning. I think it's kind of challenging because you're always going to be in a position, where deep learning is designed to just be as flexible as possible. I'm sort of throw more math at the problem, because there may be are things that your sort of simpler model doesn't account for. However deep learning could be, part of an explainable AI solution. If for example, it helps you identify what are important so called features to look at what are the important aspects of your data. So I don't know it depends on what you mean by AI, but are you ever going to get to the point where, you don't need humans sort of interpreting outputs, and making some sets of judgments about what a set of computer algorithms that are processing data think. I think it will take, I don't want to say I know what's going to happen 50 years from now, but I think it'll take a little while to get to the point where you don't have, to maybe apply some subject matter understanding and some human judgment to what an algorithm is putting out. >> It's really interesting we had Dr. Robert Gates on a years ago at another show, and he talked about the only guns in the U.S. military if I'm getting this right, that are automatic, that will go based on what the computer tells them to do, and start shooting are on the Korean border. But short of that there's always a person involved, before anybody hits a button which begs a question cause we've seen this on the big data, kind of curve, i think Gartner has talked about it, as we move up from kind of descriptive analytics diagnostic analytics, predictive, and then prescriptive and then hopefully autonomous. So I wonder so you're saying will still little ways in that that last little bumps going to be tough to overcome to get to the true autonomy. >> I think so and you know it's going to be very application dependent as well. So it's an interesting example to use the DMZ because that is obviously also a very, mission critical I would say example but in general I think that you'll see autonomy. You already do see autonomy in certain places, where I would say the States are lower. So if I'm going to have some kind of recommendation engine, that suggests if you look at the sweater maybe like that one, the risk of getting that wrong. And so fully automating that as a little bit lower, because the risk is you don't buy the sweater. I lose a little bit of income I lose a little bit of revenue as a retailer, but the risk of I make that turn, because I'm going to autonomous vehicle as much higher. So I think that you will see the progression up that curve being highly dependent on what's at stake, with different degrees of automation. That being said you will also see in certain places where there's, it's either really expensive or it's humans aren't doing a great job. You may actually start to see some mission critical automation. But those would be the places where you're seeing them. And actually I think that's one of the reasons why you see actually a lot more autonomy, in the agriculture space, than you do in the sort of passenger vehicle space. Because there's a lot at stake and it's very difficult for human beings to sort of drive large combines. >> plus they have a real they have a controlled environment. So I've interviewed Caterpillar they're doing a ton of stuff with autonomy. Cause they're there control that field, where those things are operating, and whether it's a field or a mine, it's actually fascinating how far they've come with autonomy. But let me switch to a different industry that I know is closer to your heart, and looking at some other interviews and let's talk about diagnosing disease. And if we take something specific like reviewing x-rays where the computer, and it also brings in the whole computer vision and bringing in computer vision algorithms, excuse me they can see things probably fast or do a lot more comparisons, than potentially a human doctor can. And or hopefully this whole signal to noise conversation elevate the signal for the doctor to review, and suppress the noise it's really not worth their time. They can also review a lot of literature, and hopefully bring a broader potential perspective of potential diagnoses within a set of symptoms. You said before you both your folks are physicians, and there's a certain kind of magic, a nuance, almost like kind of more childlike exploration to try to get out of the algorithm if you will to think outside the box. I wonder if you can share that, synergy between using computers and AI and machine learning to do really arduous nasty things, like going through lots and lots and lots and lots of, x-rays compared to and how that helps with, doctor who's got a whole different kind of set of experience a whole different kind of empathy, whole different type of relationship with that patient, than just a bunch of pictures of their heart or their lungs. >> I think that one of the things is, and this kind of goes back to this question of, is AI for decision support versus automation? And I think that what AI can do, and what we're pretty good at these days, with computer vision is picking up on subtle patterns right now especially if you have a very large data set. So if I can train on lots of pictures of lungs, it's a lot easier for me to identify the pictures that somehow these are not like the other ones. And that can be helpful but I think then to really interpret what you're seeing and understand is this. Is it actually bad quality image? Is it some kind of some kind of medical issue? And what is the medical issue? I think that's where bringing in, a lot of different types of knowledge, and a lot of different pieces of information. Right now I think humans are a little bit better at doing that. And some of that's because I don't think we have great ways to train on, sort of sparse datasets I guess. And the second part is that human beings might be 40 years of training a model. They 50 years of training a model as opposed to six months, or something with sparse information. That's another thing that human beings have their sort of lived experience, and the data that they bring to bear, on any type of prediction or classification is actually more than just say what they saw in their medical training. It might be the people they've met, the places they've lived what have you. And I think that's that part that sort of broader set of learning, and how things that might not be related might actually be related to your understanding of what you're looking at. I think we've got a ways to go from a sort of artificial intelligence perspective and developed. >> But it is Exascale day. And we all know about the compound exponential curves on the computing side. But let's shift gears a little bit. I know you're interested in emerging technology to support this effort, and there's so much going on in terms of, kind of the atomization of compute store and networking to be able to break it down into smaller, smaller pieces, so that you can really scale the amount of horsepower that you need to apply to a problem, to very big or to very small. Obviously the stuff that you work is more big than small. Work on GPU a lot of activity there. So I wonder if you could share, some of the emerging technologies that you're excited about to bring again more tools to the task. >> I mean, one of the areas I personally spend a lot of my time exploring are, I guess this word gets used a lot, the Cambrian  explosion of new AI accelerators. New types of chips that are really designed for different types of AI workloads. And as you sort of talked about going down, and it's almost in a way where we were sort of going back and looking at these large systems, but then exploring each little component on them, and trying to really optimize that or understand how that component contributes to the overall performance of the whole. And I think one of the things that just, I don't even know there's probably close to a hundred active vendors in the space of developing new processors, and new types of computer chips. I think one of the things that that points to is, we're moving in the direction of generally infrastructure heterogeneity. So it used to be when you built a system you probably had one type of processor, or you probably had a pretty uniform fabric across your system you usually had, I think maybe storage we started to get tearing a little bit earlier. But now I think that what we're going to see, and we're already starting to see it with Exascale systems where you've got GPUs and CPUs on the same blades, is we're starting to see as the workloads that are running at large scales are becoming more complicated. Maybe I'm doing some simulation and then I'm running I'm training some kind of AI model, and then I'm inferring it on some other type, some other output of the simulation. I need to have the ability to do a lot of different things, and do them in at a very advanced level. Which means I need very specialized technology to do it. And I think it's an exciting time. And I think we're going to test, we're going to break a lot of things. I probably shouldn't say that in this interview, but I'm hopeful that we're going to break some stuff. We're going to push all these systems to the limit, and find out where we actually need to push a little harder. And I some of the areas I think that we're going to see that, is there We're going to want to move data, and move data off of scientific instruments, into computing, into memory, into a lot of different places. And I'm really excited to see how it plays out, and what you can do and where the limits are of what you can do with the new systems. >> Arti I could talk to you all day. I love the experience and the perspective, cause you've been doing this for a long time. So I'm going to give you the final word before we sign out and really bring it back, to a more human thing which is ethics. So one of the conversations we hear all the time, is that if you are going to do something, if you're going to put together a project and you justify that project, and then you go and you collect the data and you run that algorithm and you do that project. That's great but there's like an inherent problem with, kind of data collection that may be used for something else down the road that maybe you don't even anticipate. So I just wonder if you can share, kind of top level kind of ethical take on how data scientists specifically, and then ultimately more business practitioners and other people that don't carry that title. Need to be thinking about ethics and not just kind of forget about it. That these are I had a great interview with Paul Doherty. Everybody's data is not just their data, it's it represents a person, It's a representation of what they do and how they lives. So when you think about kind of entering into a project and getting started, what do you think about in terms of the ethical considerations and how should people be cautious that they don't go places that they probably shouldn't go? >> I think that's a great question out a short answer. But I think that I honestly don't know that we have a great solutions right now, but I think that the best we can do is take a very multifaceted, and also vigilant approach to it. So when you're collecting data, and often we should remember a lot of the data that gets used isn't necessarily collected for the purpose it's being used, because we might be looking at old medical records, or old any kind of transactional records whether it be from a government or a business. And so as you start to collect data or build solutions, try to think through who are all the people who might use it? And what are the possible ways in which it could be misused? And also I encourage people to think backwards. What were the biases in place that when the data were collected, you see this a lot in the criminal justice space is the historical records reflect, historical biases in our systems. And so is I there are limits to how much you can correct for previous biases, but there are some ways to do it, but you can't do it if you're not thinking about it. So I think, sort of at the outset of developing solutions, that's important but I think equally important is putting in the systems to maintain the vigilance around it. So one don't move to autonomy before you know, what potential new errors you might or new biases you might introduce into the world. And also have systems in place to constantly ask these questions. Am I perpetuating things I don't want to perpetuate? Or how can I correct for them? And be willing to scrap your system and start from scratch if you need to. >> Well Arti thank you. Thank you so much for your time. Like I said I could talk to you for days and days and days. I love the perspective and the insight and the thoughtfulness. So thank you for sharing your thoughts, as we celebrate Exascale day. >> Thank you for having me. >> My pleasure thank you. All right she's Arti I'm Jeff it's Exascale day. We're covering on the queue thanks for watching. We'll see you next time. (bright upbeat music)

>>from around the globe. It's the Q. With digital coverage >>of exa scale day made possible by Hewlett Packard Enterprise. We're back at the celebration of Exa Scale Day. This is Dave Volant, and I'm pleased to welcome to great guests Brian Dance Berries Here. Here's what The ISS Program Science office at the Johnson Space Center. And Dr Mark Fernandez is back. He's the Americas HPC technology officer at Hewlett Packard Enterprise. Gentlemen, welcome. >>Thank you. Yeah, >>well, thanks for coming on. And, Mark, Good to see you again. And, Brian, I wonder if we could start with you and talk a little bit about your role. A T. I s s program Science office as a scientist. What's happening these days? What are you working on? >>Well, it's been my privilege the last few years to be working in the, uh, research integration area of of the space station office. And that's where we're looking at all of the different sponsors NASA, the other international partners, all the sponsors within NASA, and, uh, prioritizing what research gets to go up to station. What research gets conducted in that regard. And to give you a feel for the magnitude of the task, but we're coming up now on November 2nd for the 20th anniversary of continuous human presence on station. So we've been a space faring society now for coming up on 20 years, and I would like to point out because, you know, as an old guy myself, it impresses me. That's, you know, that's 25% of the US population. Everybody under the age of 20 has never had a moment when they were alive and we didn't have people living and working in space. So Okay, I got off on a tangent there. We'll move on in that 20 years we've done 3000 experiments on station and the station has really made ah, miraculously sort of evolution from, ah, basic platform, what is now really fully functioning national lab up there with, um, commercially run research facilities all the time. I think you can think of it as the world's largest satellite bus. We have, you know, four or five instruments looking down, measuring all kinds of things in the atmosphere during Earth observation data, looking out, doing astrophysics, research, measuring cosmic rays, X ray observatory, all kinds of things, plus inside the station you've got racks and racks of experiments going on typically scores, you know, if not more than 50 experiments going on at any one time. So, you know, the topic of this event is really important. Doesn't NASA, you know, data transmission Up and down, all of the cameras going on on on station the experiments. Um, you know, one of one of those astrophysics observatory's you know, it has collected over 15 billion um uh, impact data of cosmic rays. And so the massive amounts of data that that needs to be collected and transferred for all of these experiments to go on really hits to the core. And I'm glad I'm able toe be here and and speak with you today on this. This topic. >>Well, thank you for that, Bryan. A baby boomer, right? Grew up with the national pride of the moon landing. And of course, we've we've seen we saw the space shuttle. We've seen international collaboration, and it's just always been something, you know, part of our lives. So thank you for the great work that you guys were doing their mark. You and I had a great discussion about exa scale and kind of what it means for society and some of the innovations that we could maybe expect over the coming years. Now I wonder if you could talk about some of the collaboration between what you guys were doing and Brian's team. >>Uh, yeah, so yes, indeed. Thank you for having me early. Appreciate it. That was a great introduction. Brian, Uh, I'm the principal investigator on Space Born computer, too. And as the two implies, where there was one before it. And so we worked with Bryant and his team extensively over the past few years again high performance computing on board the International Space Station. Brian mentioned the thousands of experiments that have been done to date and that there are currently 50 orm or going on at any one time. And those experiments collect data. And up until recently, you've had to transmit that data down to Earth for processing. And that's a significant amount of bandwidth. Yeah, so with baseball and computer to we're inviting hello developers and others to take advantage of that onboard computational capability you mentioned exa scale. We plan to get the extra scale next year. We're currently in the era that's called PETA scale on. We've been in the past scale era since 2000 and seven, so it's taken us a while to make it that next lead. Well, 10 years after Earth had a PETA scale system in 2017 were able to put ah teraflop system on the International space station to prove that we could do a trillion calculations a second in space. That's where the data is originating. That's where it might be best to process it. So we want to be able to take those capabilities with us. And with H. P. E. Acting as a wonderful partner with Brian and NASA and the space station, we think we're able to do that for many of these experiments. >>It's mind boggling you were talking about. I was talking about the moon landing earlier and the limited power of computing power. Now we've got, you know, water, cool supercomputers in space. I'm interested. I'd love to explore this notion of private industry developing space capable computers. I think it's an interesting model where you have computer companies can repurpose technology that they're selling obviously greater scale for space exploration and apply that supercomputing technology instead of having government fund, proprietary purpose built systems that air. Essentially, you use case, if you will. So, Brian, what are the benefits of that model? The perhaps you wouldn't achieve with governments or maybe contractors, you know, kind of building these proprietary systems. >>Well, first of all, you know, any any tool, your using any, any new technology that has, you know, multiple users is going to mature quicker. You're gonna have, you know, greater features, greater capabilities, you know, not even talking about computers. Anything you're doing. So moving from, you know, governor government is a single, um, you know, user to off the shelf type products gives you that opportunity to have things that have been proven, have the technology is fully matured. Now, what had to happen is we had to mature the space station so that we had a platform where we could test these things and make sure they're gonna work in the high radiation environments, you know, And they're gonna be reliable, because first, you've got to make sure that that safety and reliability or taken care of so that that's that's why in the space program you're gonna you're gonna be behind the times in terms of the computing power of the equipment up there because, first of all and foremost, you needed to make sure that it was reliable and say, Now, my undergraduate degree was in aerospace engineering and what we care about is aerospace engineers is how heavy is it, how big and bulky is it because you know it z expensive? You know, every pound I once visited Gulfstream Aerospace, and they would pay their employees $1000 that they could come up with a way saving £1 in building that aircraft. That means you have more capacity for flying. It's on the orders of magnitude. More important to do that when you're taking payloads to space. So you know, particularly with space born computer, the opportunity there to use software and and check the reliability that way, Uh, without having to make the computer, you know, radiation resistance, if you will, with heavy, you know, bulky, um, packaging to protect it from that radiation is a really important thing, and it's gonna be a huge advantage moving forward as we go to the moon and on to Mars. >>Yeah, that's interesting. I mean, your point about cots commercial off the shelf technology. I mean, that's something that obviously governments have wanted to leverage for a long, long time for many, many decades. But but But Mark the issue was always the is. Brian was just saying the very stringent and difficult requirements of space. Well, you're obviously with space Born one. You got to the point where you had visibility of the economics made sense. It made commercial sense for companies like Hewlett Packard Enterprise. And now we've sort of closed that gap to the point where you're sort of now on that innovation curve. What if you could talk about that a little bit? >>Yeah, absolutely. Brian has some excellent points, you know, he said, anything we do today and requires computers, and that's absolutely correct. So I tell people that when you go to the moon and when you go to the Mars, you probably want to go with the iPhone 10 or 11 and not a flip phone. So before space born was sent up, you went with 2000 early two thousands computing technology there which, like you said many of the people born today weren't even around when the space station began and has been occupied so they don't even know how to program or use that type of computing. Power was based on one. We sent the exact same products that we were shipping to customers today, so they are current state of the art, and we had a mandate. Don't touch the hardware, have all the protection that you can via software. So that's what we've done. We've got several philosophical ways to do that. We've implemented those in software. They've been successful improving in the space for one, and now it's space born to. We're going to begin the experiments so that the rest of the community so that the rest of the community can figure out that it is economically viable, and it will accelerate their research and progress in space. I'm most excited about that. Every venture into space as Brian mentioned will require some computational capability, and HP has figured out that the economics air there we need to bring the customers through space ball into in order for them to learn that we are reliable but current state of the art, and that we could benefit them and all of humanity. >>Guys, I wanna ask you kind of a two part question. And, Brian, I'll start with you and it z somewhat philosophical. Uh, I mean, my understanding was and I want to say this was probably around the time of the Bush administration w two on and maybe certainly before that, but as technology progress, there was a debate about all right, Should we put our resource is on moon because of the proximity to Earth? Or should we, you know, go where no man has gone before and or woman and get to Mars? Where What's the thinking today, Brian? On that? That balance between Moon and Mars? >>Well, you know, our plans today are are to get back to the moon by 2024. That's the Artemus program. Uh, it's exciting. It makes sense from, you know, an engineering standpoint. You take, you know, you take baby steps as you continue to move forward. And so you have that opportunity, um, to to learn while you're still, you know, relatively close to home. You can get there in days, not months. If you're going to Mars, for example, toe have everything line up properly. You're looking at a multi year mission you know, it may take you nine months to get there. Then you have to wait for the Earth and Mars to get back in the right position to come back on that same kind of trajectory. So you have toe be there for more than a year before you can turn around and come back. So, you know, he was talking about the computing power. You know, right now that the beautiful thing about the space station is, it's right there. It's it's orbiting above us. It's only 250 miles away. Uh, so you can test out all of these technologies. You can rely on the ground to keep track of systems. There's not that much of a delay in terms of telemetry coming back. But as you get to the moon and then definitely is, you get get out to Mars. You know, there are enough minutes delay out there that you've got to take the computing power with you. You've got to take everything you need to be able to make those decisions you need to make because there's not time to, um, you know, get that information back on the ground, get back get it back to Earth, have people analyze the situation and then tell you what the next step is to do. That may be too late. So you've got to think the computing power with you. >>So extra scale bring some new possibilities. Both both for, you know, the moon and Mars. I know Space Born one did some simulations relative. Tomorrow we'll talk about that. But But, Brian, what are the things that you hope to get out of excess scale computing that maybe you couldn't do with previous generations? >>Well, you know, you know, market on a key point. You know, bandwidth up and down is, of course, always a limitation. In the more computing data analysis you can do on site, the more efficient you could be with parsing out that that bandwidth and to give you ah, feel for just that kind of think about those those observatory's earth observing and an astronomical I was talking about collecting data. Think about the hours of video that are being recorded daily as the astronauts work on various things to document what they're doing. They many of the biological experiments, one of the key key pieces of data that's coming back. Is that video of the the microbes growing or the plants growing or whatever fluid physics experiments going on? We do a lot of colloids research, which is suspended particles inside ah liquid. And that, of course, high speed video. Is he Thio doing that kind of research? Right now? We've got something called the I s s experience going on in there, which is basically recording and will eventually put out a syriza of basically a movie on virtual reality recording. That kind of data is so huge when you have a 360 degree camera up there recording all of that data, great virtual reality, they There's still a lot of times bringing that back on higher hard drives when the space six vehicles come back to the Earth. That's a lot of data going on. We recorded videos all the time, tremendous amount of bandwidth going on. And as you get to the moon and as you get further out, you can a man imagine how much more limiting that bandwidth it. >>Yeah, We used to joke in the old mainframe days that the fastest way to get data from point a to Point B was called C Tam, the Chevy truck access method. Just load >>up a >>truck, whatever it was, tapes or hard drive. So eso and mark, of course space born to was coming on. Spaceport one really was a pilot, but it proved that the commercial computers could actually work for long durations in space, and the economics were feasible. Thinking about, you know, future missions and space born to What are you hoping to accomplish? >>I'm hoping to bring. I'm hoping to bring that success from space born one to the rest of the community with space born to so that they can realize they can do. They're processing at the edge. The purpose of exploration is insight, not data collection. So all of these experiments begin with data collection. Whether that's videos or samples are mold growing, etcetera, collecting that data, we must process it to turn it into information and insight. And the faster we can do that, the faster we get. Our results and the better things are. I often talk Thio College in high school and sometimes grammar school students about this need to process at the edge and how the communication issues can prevent you from doing that. For example, many of us remember the communications with the moon. The moon is about 250,000 miles away, if I remember correctly, and the speed of light is 186,000 miles a second. So even if the speed of light it takes more than a second for the communications to get to the moon and back. So I can remember being stressed out when Houston will to make a statement. And we were wondering if the astronauts could answer Well, they answered as soon as possible. But that 1 to 2 second delay that was natural was what drove us crazy, which made us nervous. We were worried about them in the success of the mission. So Mars is millions of miles away. So flip it around. If you're a Mars explorer and you look out the window and there's a big red cloud coming at you that looks like a tornado and you might want to do some Mars dust storm modeling right then and there to figure out what's the safest thing to do. You don't have the time literally get that back to earth have been processing and get you the answer back. You've got to take those computational capabilities with you. And we're hoping that of these 52 thousands of experiments that are on board, the SS can show that in order to better accomplish their missions on the moon. And Omar, >>I'm so glad you brought that up because I was gonna ask you guys in the commercial world everybody talks about real time. Of course, we talk about the real time edge and AI influencing and and the time value of data I was gonna ask, you know, the real time, Nous, How do you handle that? I think Mark, you just answered that. But at the same time, people will say, you know, the commercial would like, for instance, in advertising. You know, the joke the best. It's not kind of a joke, but the best minds of our generation tryingto get people to click on ads. And it's somewhat true, unfortunately, but at any rate, the value of data diminishes over time. I would imagine in space exploration where where you're dealing and things like light years, that actually there's quite a bit of value in the historical data. But, Mark, you just You just gave a great example of where you need real time, compute capabilities on the ground. But but But, Brian, I wonder if I could ask you the value of this historic historical data, as you just described collecting so much data. Are you? Do you see that the value of that data actually persists over time, you could go back with better modeling and better a i and computing and actually learn from all that data. What are your thoughts on that, Brian? >>Definitely. I think the answer is yes to that. And, you know, as part of the evolution from from basically a platform to a station, we're also learning to make use of the experiments in the data that we have there. NASA has set up. Um, you know, unopened data access sites for some of our physical science experiments that taking place there and and gene lab for looking at some of the biological genomic experiments that have gone on. And I've seen papers already beginning to be generated not from the original experimenters and principal investigators, but from that data set that has been collected. And, you know, when you're sending something up to space and it to the space station and volume for cargo is so limited, you want to get the most you can out of that. So you you want to be is efficient as possible. And one of the ways you do that is you collect. You take these earth observing, uh, instruments. Then you take that data. And, sure, the principal investigators air using it for the key thing that they designed it for. But if that data is available, others will come along and make use of it in different ways. >>Yeah, So I wanna remind the audience and these these these air supercomputers, the space born computers, they're they're solar powered, obviously, and and they're mounted overhead, right? Is that is that correct? >>Yeah. Yes. Space borne computer was mounted in the overhead. I jokingly say that as soon as someone could figure out how to get a data center in orbit, they will have a 50 per cent denser data station that we could have down here instead of two robes side by side. You can also have one overhead on. The power is free. If you can drive it off a solar, and the cooling is free because it's pretty cold out there in space, so it's gonna be very efficient. Uh, space borne computer is the most energy efficient computer in existence. Uh, free electricity and free cooling. And now we're offering free cycles through all the experimenters on goal >>Eso Space born one exceeded its mission timeframe. You were able to run as it was mentioned before some simulations for future Mars missions. And, um and you talked a little bit about what you want to get out of, uh, space born to. I mean, are there other, like, wish list items, bucket bucket list items that people are talking about? >>Yeah, two of them. And these air kind of hypothetical. And Brian kind of alluded to them. Uh, one is having the data on board. So an example that halo developers talk to us about is Hey, I'm on Mars and I see this mold growing on my potatoes. That's not good. So let me let me sample that mold, do a gene sequencing, and then I've got stored all the historical data on space borne computer of all the bad molds out there and let me do a comparison right then and there before I have dinner with my fried potato. So that's that's one. That's very interesting. A second one closely related to it is we have offered up the storage on space borne computer to for all of your raw data that we process. So, Mr Scientist, if if you need the raw data and you need it now, of course, you can have it sent down. But if you don't let us just hold it there as long as they have space. And when we returned to Earth like you mentioned, Patrick will ship that solid state disk back to them so they could have a new person, but again, reserving that network bandwidth, uh, keeping all that raw data available for the entire duration of the mission so that it may have value later on. >>Great. Thank you for that. I want to end on just sort of talking about come back to the collaboration between I S s National Labs and Hewlett Packard Enterprise, and you've got your inviting project ideas using space Bourne to during the upcoming mission. Maybe you could talk about what that's about, and we have A We have a graphic we're gonna put up on DSM information that you can you can access. But please, mark share with us what you're planning there. >>So again, the collaboration has been outstanding. There. There's been a mention off How much savings is, uh, if you can reduce the weight by a pound. Well, our partners ice s national lab and NASA have taken on that cost of delivering baseball in computer to the international space station as part of their collaboration and powering and cooling us and giving us the technical support in return on our side, we're offering up space borne computer to for all the onboard experiments and all those that think they might be wanting doing experiments on space born on the S s in the future to take advantage of that. So we're very, very excited about that. >>Yeah, and you could go toe just email space born at hp dot com on just float some ideas. I'm sure at some point there'll be a website so you can email them or you can email me david dot volonte at at silicon angle dot com and I'll shoot you that that email one or that website once we get it. But, Brian, I wanna end with you. You've been so gracious with your time. Uh, yeah. Give us your final thoughts on on exa scale. Maybe how you're celebrating exa scale day? I was joking with Mark. Maybe we got a special exa scale drink for 10. 18 but, uh, what's your final thoughts, Brian? >>Uh, I'm going to digress just a little bit. I think I think I have a unique perspective to celebrate eggs a scale day because as an undergraduate student, I was interning at Langley Research Center in the wind tunnels and the wind tunnel. I was then, um, they they were very excited that they had a new state of the art giant room size computer to take that data we way worked on unsteady, um, aerodynamic forces. So you need a lot of computation, and you need to be ableto take data at a high bandwidth. To be able to do that, they'd always, you know, run their their wind tunnel for four or five hours. Almost the whole shift. Like that data and maybe a week later, been ableto look at the data to decide if they got what they were looking for? Well, at the time in the in the early eighties, this is definitely the before times that I got there. They had they had that computer in place. Yes, it was a punchcard computer. It was the one time in my life I got to put my hands on the punch cards and was told not to drop them there. Any trouble if I did that. But I was able thio immediately after, uh, actually, during their run, take that data, reduce it down, grabbed my colored pencils and graph paper and graph out coefficient lift coefficient of drag. Other things that they were measuring. Take it back to them. And they were so excited to have data two hours after they had taken it analyzed and looked at it just pickled them. Think that they could make decisions now on what they wanted to do for their next run. Well, we've come a long way since then. You know, extra scale day really, really emphasizes that point, you know? So it really brings it home to me. Yeah. >>Please, no, please carry on. >>Well, I was just gonna say, you know, you talked about the opportunities that that space borne computer provides and and Mark mentioned our colleagues at the I S s national lab. You know, um, the space station has been declared a national laboratory, and so about half of the, uh, capabilities we have for doing research is a portion to the national lab so that commercial entities so that HP can can do these sorts of projects and universities can access station and and other government agencies. And then NASA can focus in on those things we want to do purely to push our exploration programs. So the opportunities to take advantage of that are there marks opening up the door for a lot of opportunities. But others can just Google S s national laboratory and find some information on how to get in the way. Mark did originally using s national lab to maybe get a good experiment up there. >>Well, it's just astounding to see the progress that this industry is made when you go back and look, you know, the early days of supercomputing to imagine that they actually can be space born is just tremendous. Not only the impacts that it can have on Space six exploration, but also society in general. Mark Wayne talked about that. Guys, thanks so much for coming on the Cube and celebrating Exa scale day and helping expand the community. Great work. And, uh, thank you very much for all that you guys dio >>Thank you very much for having me on and everybody out there. Let's get the XO scale as quick as we can. Appreciate everything you all are >>doing. Let's do it. >>I've got a I've got a similar story. Humanity saw the first trillion calculations per second. Like I said in 1997. And it was over 100 racks of computer equipment. Well, space borne one is less than fourth of Iraq in only 20 years. So I'm gonna be celebrating exa scale day in anticipation off exa scale computers on earth and soon following within the national lab that exists in 20 plus years And being on Mars. >>That's awesome. That mark. Thank you for that. And and thank you for watching everybody. We're celebrating Exa scale day with the community. The supercomputing community on the Cube Right back

>>from around the globe. It's the Q with digital coverage of exa scale day made possible by Hewlett Packard Enterprise. Welcome, everyone to the Cube celebration of Exa Scale Day. Shaheen Khan is here. He's the founding partner, an analyst at Orion X And, among other things, he is the co host of Radio free HPC Shaheen. Welcome. Thanks for coming on. >>Thanks for being here, Dave. Great to be here. How are you >>doing? Well, thanks. Crazy with doing these things, Cove in remote interviews. I wish we were face to face at us at a supercomputer show, but, hey, this thing is working. We can still have great conversations. And And I love talking to analysts like you because you bring an independent perspective. You're very wide observation space. So So let me, Like many analysts, you probably have sort of a mental model or a market model that you look at. So maybe talk about your your work, how you look at the market, and we could get into some of the mega trends that you see >>very well. Very well. Let me just quickly set the scene. We fundamentally track the megatrends of the Information Age And, of course, because we're in the information age, digital transformation falls out of that. And the megatrends that drive that in our mind is Ayotte, because that's the fountain of data five G. Because that's how it's gonna get communicated ai and HBC because that's how we're gonna make sense of it Blockchain and Cryptocurrencies because that's how it's gonna get transacted on. That's how value is going to get transferred from the place took place and then finally, quantum computing, because that exemplifies how things are gonna get accelerated. >>So let me ask you So I spent a lot of time, but I D. C and I had the pleasure of of the High Performance computing group reported into me. I wasn't an HPC analyst, but over time you listen to those guys, you learning. And as I recall, it was HPC was everywhere, and it sounds like we're still seeing that trend where, whether it was, you know, the Internet itself were certainly big data, you know, coming into play. Uh, you know, defense, obviously. But is your background mawr HPC or so that these other technologies that you're talking about it sounds like it's your high performance computing expert market watcher. And then you see it permeating into all these trends. Is that a fair statement? >>That's a fair statement. I did grow up in HPC. My first job out of school was working for an IBM fellow doing payroll processing in the old days on and and And it went from there, I worked for Cray Research. I worked for floating point systems, so I grew up in HPC. But then, over time, uh, we had experiences outside of HPC. So for a number of years, I had to go do commercial enterprise computing and learn about transaction processing and business intelligence and, you know, data warehousing and things like that, and then e commerce and then Web technology. So over time it's sort of expanded. But HPC is a like a bug. You get it and you can't get rid of because it's just so inspiring. So supercomputing has always been my home, so to say >>well and so the reason I ask is I wanted to touch on a little history of the industry is there was kind of a renaissance in many, many years ago, and you had all these startups you had Kendall Square Research Danny Hillis thinking machines. You had convex trying to make many supercomputers. And it was just this This is, you know, tons of money flowing in and and then, you know, things kind of consolidate a little bit and, uh, things got very, very specialized. And then with the big data craze, you know, we've seen HPC really at the heart of all that. So what's your take on on the ebb and flow of the HPC business and how it's evolved? >>Well, HBC was always trying to make sense of the world, was trying to make sense of nature. And of course, as much as we do know about nature, there's a lot we don't know about nature and problems in nature are you can classify those problems into basically linear and nonlinear problems. The linear ones are easy. They've already been solved. The nonlinear wants. Some of them are easy. Many of them are hard, the nonlinear, hard, chaotic. All of those problems are the ones that you really need to solve. The closer you get. So HBC was basically marching along trying to solve these things. It had a whole process, you know, with the scientific method going way back to Galileo, the experimentation that was part of it. And then between theory, you got to look at the experiment and the data. You kind of theorize things. And then you experimented to prove the theories and then simulation and using the computers to validate some things eventually became a third pillar of off science. On you had theory, experiment and simulation. So all of that was going on until the rest of the world, thanks to digitization, started needing some of those same techniques. Why? Because you've got too much data. Simply, there's too much data to ship to the cloud. There's too much data to, uh, make sense of without math and science. So now enterprise computing problems are starting to look like scientific problems. Enterprise data centers are starting to look like national lab data centers, and there is that sort of a convergence that has been taking place gradually, really over the past 34 decades. And it's starting to look really, really now >>interesting, I want I want to ask you about. I was like to talk to analysts about, you know, competition. The competitive landscape is the competition in HPC. Is it between vendors or countries? >>Well, this is a very interesting thing you're saying, because our other thesis is that we are moving a little bit beyond geopolitics to techno politics. And there are now, uh, imperatives at the political level that are driving some of these decisions. Obviously, five G is very visible as as as a piece of technology that is now in the middle of political discussions. Covert 19 as you mentioned itself, is a challenge that is a global challenge that needs to be solved at that level. Ai, who has access to how much data and what sort of algorithms. And it turns out as we all know that for a I, you need a lot more data than you thought. You do so suddenly. Data superiority is more important perhaps than even. It can lead to information superiority. So, yeah, that's really all happening. But the actors, of course, continue to be the vendors that are the embodiment of the algorithms and the data and the systems and infrastructure that feed the applications. So to say >>so let's get into some of these mega trends, and maybe I'll ask you some Colombo questions and weaken geek out a little bit. Let's start with a you know, again, it was one of this when I started the industry. It's all it was a i expert systems. It was all the rage. And then we should have had this long ai winter, even though, you know, the technology never went away. But But there were at least two things that happened. You had all this data on then the cost of computing. You know, declines came down so so rapidly over the years. So now a eyes back, we're seeing all kinds of applications getting infused into virtually every part of our lives. People trying to advertise to us, etcetera. Eso So talk about the intersection of AI and HPC. What are you seeing there? >>Yeah, definitely. Like you said, I has a long history. I mean, you know, it came out of MIT Media Lab and the AI Lab that they had back then and it was really, as you mentioned, all focused on expert systems. It was about logical processing. It was a lot of if then else. And then it morphed into search. How do I search for the right answer, you know, needle in the haystack. But then, at some point, it became computational. Neural nets are not a new idea. I remember you know, we had we had a We had a researcher in our lab who was doing neural networks, you know, years ago. And he was just saying how he was running out of computational power and we couldn't. We were wondering, you know what? What's taking all this difficult, You know, time. And it turns out that it is computational. So when deep neural nets showed up about a decade ago, arm or it finally started working and it was a confluence of a few things. Thalib rhythms were there, the data sets were there, and the technology was there in the form of GPS and accelerators that finally made distractible. So you really could say, as in I do say that a I was kind of languishing for decades before HPC Technologies reignited it. And when you look at deep learning, which is really the only part of a I that has been prominent and has made all this stuff work, it's all HPC. It's all matrix algebra. It's all signal processing algorithms. are computational. The infrastructure is similar to H B. C. The skill set that you need is the skill set of HPC. I see a lot of interest in HBC talent right now in part motivated by a I >>mhm awesome. Thank you on. Then I wanna talk about Blockchain and I can't talk about Blockchain without talking about crypto you've written. You've written about that? I think, you know, obviously supercomputers play a role. I think you had written that 50 of the top crypto supercomputers actually reside in in China A lot of times the vendor community doesn't like to talk about crypto because you know that you know the fraud and everything else. But it's one of the more interesting use cases is actually the primary use case for Blockchain even though Blockchain has so much other potential. But what do you see in Blockchain? The potential of that technology And maybe we can work in a little crypto talk as well. >>Yeah, I think 11 simple way to think of Blockchain is in terms off so called permission and permission less the permission block chains or when everybody kind of knows everybody and you don't really get to participate without people knowing who you are and as a result, have some basis to trust your behavior and your transactions. So things are a lot calmer. It's a lot easier. You don't really need all the supercomputing activity. Whereas for AI the assertion was that intelligence is computer herbal. And with some of these exa scale technologies, we're trying to, you know, we're getting to that point for permission. Less Blockchain. The assertion is that trust is computer ble and, it turns out for trust to be computer ble. It's really computational intensive because you want to provide an incentive based such that good actors are rewarded and back actors. Bad actors are punished, and it is worth their while to actually put all their effort towards good behavior. And that's really what you see, embodied in like a Bitcoin system where the chain has been safe over the many years. It's been no attacks, no breeches. Now people have lost money because they forgot the password or some other. You know, custody of the accounts have not been trustable, but the chain itself has managed to produce that, So that's an example of computational intensity yielding trust. So that suddenly becomes really interesting intelligence trust. What else is computer ble that we could do if we if we had enough power? >>Well, that's really interesting the way you described it, essentially the the confluence of crypto graphics software engineering and, uh, game theory, Really? Where the bad actors air Incentive Thio mined Bitcoin versus rip people off because it's because because there are lives better eso eso so that so So Okay, so make it make the connection. I mean, you sort of did. But But I want to better understand the connection between, you know, supercomputing and HPC and Blockchain. We know we get a crypto for sure, like in mind a Bitcoin which gets harder and harder and harder. Um and you mentioned there's other things that we can potentially compute on trust. Like what? What else? What do you thinking there? >>Well, I think that, you know, the next big thing that we are really seeing is in communication. And it turns out, as I was saying earlier, that these highly computational intensive algorithms and models show up in all sorts of places like, you know, in five g communication, there's something called the memo multi and multi out and to optimally manage that traffic such that you know exactly what beam it's going to and worth Antenna is coming from that turns out to be a non trivial, you know, partial differential equation. So next thing you know, you've got HPC in there as and he didn't expect it because there's so much data to be sent, you really have to do some data reduction and data processing almost at the point of inception, if not at the point of aggregation. So that has led to edge computing and edge data centers. And that, too, is now. People want some level of computational capability at that place like you're building a microcontroller, which traditionally would just be a, you know, small, low power, low cost thing. And people want victor instructions. There. People want matrix algebra there because it makes sense to process the data before you have to ship it. So HPCs cropping up really everywhere. And then finally, when you're trying to accelerate things that obviously GP use have been a great example of that mixed signal technologies air coming to do analog and digital at the same time, quantum technologies coming so you could do the you know, the usual analysts to buy to where you have analog, digital, classical quantum and then see which, you know, with what lies where all of that is coming. And all of that is essentially resting on HBC. >>That's interesting. I didn't realize that HBC had that position in five G with multi and multi out. That's great example and then I o t. I want to ask you about that because there's a lot of discussion about real time influencing AI influencing at the edge on you're seeing sort of new computing architectures, potentially emerging, uh, video. The acquisition of arm Perhaps, you know, amore efficient way, maybe a lower cost way of doing specialized computing at the edge it, But it sounds like you're envisioning, actually, supercomputing at the edge. Of course, we've talked to Dr Mark Fernandez about space born computers. That's like the ultimate edge you got. You have supercomputers hanging on the ceiling of the International space station, but But how far away are we from this sort of edge? Maybe not. Space is an extreme example, but you think factories and windmills and all kinds of edge examples where supercomputing is is playing a local role. >>Well, I think initially you're going to see it on base stations, Antenna towers, where you're aggregating data from a large number of endpoints and sensors that are gathering the data, maybe do some level of local processing and then ship it to the local antenna because it's no more than 100 m away sort of a thing. But there is enough there that that thing can now do the processing and do some level of learning and decide what data to ship back to the cloud and what data to get rid of and what data to just hold. Or now those edge data centers sitting on top of an antenna. They could have a half a dozen GPS in them. They're pretty powerful things. They could have, you know, one they could have to, but but it could be depending on what you do. A good a good case study. There is like surveillance cameras. You don't really need to ship every image back to the cloud. And if you ever need it, the guy who needs it is gonna be on the scene, not back at the cloud. So there is really no sense in sending it, Not certainly not every frame. So maybe you can do some processing and send an image every five seconds or every 10 seconds, and that way you can have a record of it. But you've reduced your bandwidth by orders of magnitude. So things like that are happening. And toe make sense of all of that is to recognize when things changed. Did somebody come into the scene or is it just you know that you know, they became night, So that's sort of a decision. Cannot be automated and fundamentally what is making it happen? It may not be supercomputing exa scale class, but it's definitely HPCs, definitely numerically oriented technologies. >>Shane, what do you see happening in chip architectures? Because, you see, you know the classical intel they're trying to put as much function on the real estate as possible. We've seen the emergence of alternative processors, particularly, uh, GP use. But even if f b g A s, I mentioned the arm acquisition, so you're seeing these alternative processors really gain momentum and you're seeing data processing units emerge and kind of interesting trends going on there. What do you see? And what's the relationship to HPC? >>Well, I think a few things are going on there. Of course, one is, uh, essentially the end of Moore's law, where you cannot make the cycle time be any faster, so you have to do architectural adjustments. And then if you have a killer app that lends itself to large volume, you can build silicon. That is especially good for that now. Graphics and gaming was an example of that, and people said, Oh my God, I've got all these cores in there. Why can't I use it for computation? So everybody got busy making it 64 bit capable and some grass capability, And then people say, Oh, I know I can use that for a I And you know, now you move it to a I say, Well, I don't really need 64 but maybe I can do it in 32 or 16. So now you do it for that, and then tens, of course, come about. And so there's that sort of a progression of architecture, er trumping, basically cycle time. That's one thing. The second thing is scale out and decentralization and distributed computing. And that means that the inter communication and intra communication among all these notes now becomes an issue big enough issue that maybe it makes sense to go to a DPU. Maybe it makes sense to go do some level of, you know, edge data centers like we were talking about on then. The third thing, really is that in many of these cases you have data streaming. What is really coming from I o t, especially an edge, is that data is streaming and when data streaming suddenly new architectures like F B G. A s become really interesting and and and hold promise. So I do see, I do see FPG's becoming more prominent just for that reason, but then finally got a program all of these things on. That's really a difficulty, because what happens now is that you need to get three different ecosystems together mobile programming, embedded programming and cloud programming. And those are really three different developer types. You can't hire somebody who's good at all three. I mean, maybe you can, but not many. So all of that is challenges that are driving this this this this industry, >>you kind of referred to this distributed network and a lot of people you know, they refer to this. The next generation cloud is this hyper distributed system. When you include the edge and multiple clouds that etcetera space, maybe that's too extreme. But to your point, at least I inferred there's a There's an issue of Leighton. See, there's the speed of light s So what? What? What is the implication then for HBC? Does that mean I have tow Have all the data in one place? Can I move the compute to the data architecturally, What are you seeing there? >>Well, you fundamentally want to optimize when to move data and when to move, Compute. Right. So is it better to move data to compute? Or is it better to bring compute to data and under what conditions? And the dancer is gonna be different for different use cases. It's like, really, is it worth my while to make the trip, get my processing done and then come back? Or should I just developed processing capability right here? Moving data is really expensive and relatively speaking. It has become even more expensive, while the price of everything has dropped down its price has dropped less than than than like processing. So it is now starting to make sense to do a lot of local processing because processing is cheap and moving data is expensive Deep Use an example of that, Uh, you know, we call this in C two processing like, you know, let's not move data. If you don't have to accept that we live in the age of big data, so data is huge and wants to be moved. And that optimization, I think, is part of what you're what you're referring to. >>Yeah, So a couple examples might be autonomous vehicles. You gotta have to make decisions in real time. You can't send data back to the cloud flip side of that is we talk about space borne computers. You're collecting all this data You can at some point. You know, maybe it's a year or two after the lived out its purpose. You ship that data back and a bunch of disk drives or flash drives, and then load it up into some kind of HPC system and then have at it and then you doom or modeling and learn from that data corpus, right? I mean those air, >>right? Exactly. Exactly. Yeah. I mean, you know, driverless vehicles is a great example, because it is obviously coming fast and furious, no pun intended. And also, it dovetails nicely with the smart city, which dovetails nicely with I o. T. Because it is in an urban area. Mostly, you can afford to have a lot of antenna, so you can give it the five g density that you want. And it requires the Layton sees. There's a notion of how about if my fleet could communicate with each other. What if the car in front of me could let me know what it sees, That sort of a thing. So, you know, vehicle fleets is going to be in a non opportunity. All of that can bring all of what we talked about. 21 place. >>Well, that's interesting. Okay, so yeah, the fleets talking to each other. So kind of a Byzantine fault. Tolerance. That problem that you talk about that z kind of cool. I wanna I wanna sort of clothes on quantum. It's hard to get your head around. Sometimes You see the demonstrations of quantum. It's not a one or zero. It could be both. And you go, What? How did come that being so? And And of course, there it's not stable. Uh, looks like it's quite a ways off, but the potential is enormous. It's of course, it's scary because we think all of our, you know, passwords are already, you know, not secure. And every password we know it's gonna get broken. But give us the give us the quantum 101 And let's talk about what the implications. >>All right, very well. So first off, we don't need to worry about our passwords quite yet. That that that's that's still ways off. It is true that analgesic DM came up that showed how quantum computers can fact arise numbers relatively fast and prime factory ization is at the core of a lot of cryptology algorithms. So if you can fact arise, you know, if you get you know, number 21 you say, Well, that's three times seven, and those three, you know, three and seven or prime numbers. Uh, that's an example of a problem that has been solved with quantum computing, but if you have an actual number, would like, you know, 2000 digits in it. That's really harder to do. It's impossible to do for existing computers and even for quantum computers. Ways off, however. So as you mentioned, cubits can be somewhere between zero and one, and you're trying to create cubits Now there are many different ways of building cubits. You can do trapped ions, trapped ion trapped atoms, photons, uh, sometimes with super cool, sometimes not super cool. But fundamentally, you're trying to get these quantum level elements or particles into a superimposed entanglement state. And there are different ways of doing that, which is why quantum computers out there are pursuing a lot of different ways. The whole somebody said it's really nice that quantum computing is simultaneously overhyped and underestimated on. And that is that is true because there's a lot of effort that is like ways off. On the other hand, it is so exciting that you don't want to miss out if it's going to get somewhere. So it is rapidly progressing, and it has now morphed into three different segments. Quantum computing, quantum communication and quantum sensing. Quantum sensing is when you can measure really precise my new things because when you perturb them the quantum effects can allow you to measure them. Quantum communication is working its way, especially in financial services, initially with quantum key distribution, where the key to your cryptography is sent in a quantum way. And the data sent a traditional way that our efforts to do quantum Internet, where you actually have a quantum photon going down the fiber optic lines and Brookhaven National Labs just now demonstrated a couple of weeks ago going pretty much across the, you know, Long Island and, like 87 miles or something. So it's really coming, and and fundamentally, it's going to be brand new algorithms. >>So these examples that you're giving these air all in the lab right there lab projects are actually >>some of them are in the lab projects. Some of them are out there. Of course, even traditional WiFi has benefited from quantum computing or quantum analysis and, you know, algorithms. But some of them are really like quantum key distribution. If you're a bank in New York City, you very well could go to a company and by quantum key distribution services and ship it across the you know, the waters to New Jersey on that is happening right now. Some researchers in China and Austria showed a quantum connection from, like somewhere in China, to Vienna, even as far away as that. When you then put the satellite and the nano satellites and you know, the bent pipe networks that are being talked about out there, that brings another flavor to it. So, yes, some of it is like real. Some of it is still kind of in the last. >>How about I said I would end the quantum? I just e wanna ask you mentioned earlier that sort of the geopolitical battles that are going on, who's who are the ones to watch in the Who? The horses on the track, obviously United States, China, Japan. Still pretty prominent. How is that shaping up in your >>view? Well, without a doubt, it's the US is to lose because it's got the density and the breadth and depth of all the technologies across the board. On the other hand, information age is a new eyes. Their revolution information revolution is is not trivial. And when revolutions happen, unpredictable things happen, so you gotta get it right and and one of the things that these technologies enforce one of these. These revolutions enforce is not just kind of technological and social and governance, but also culture, right? The example I give is that if you're a farmer, it takes you maybe a couple of seasons before you realize that you better get up at the crack of dawn and you better do it in this particular season. You're gonna starve six months later. So you do that to three years in a row. A culture has now been enforced on you because that's how it needs. And then when you go to industrialization, you realize that Gosh, I need these factories. And then, you know I need workers. And then next thing you know, you got 9 to 5 jobs and you didn't have that before. You don't have a command and control system. You had it in military, but not in business. And and some of those cultural shifts take place on and change. So I think the winner is going to be whoever shows the most agility in terms off cultural norms and governance and and and pursuit of actual knowledge and not being distracted by what you think. But what actually happens and Gosh, I think these exa scale technologies can make the difference. >>Shaheen Khan. Great cast. Thank you so much for joining us to celebrate the extra scale day, which is, uh, on 10. 18 on dso. Really? Appreciate your insights. >>Likewise. Thank you so much. >>All right. Thank you for watching. Keep it right there. We'll be back with our next guest right here in the Cube. We're celebrating Exa scale day right back.

>> Hello everyone. On today's panel, the theme is Drug Discovery and how Artificial Intelligence can make a difference. On the panel today, we are honored to have Dr. Ryan Yates, principal scientist at The National Center for Natural Products Research, with a focus on botanicals specifically the pharmacokinetics, which is essentially how the drug changes over time in our body and pharmacodynamics which is essentially how drugs affects our body. And of particular interest to him is the use of AI in preclinical screening models to identify chemical combinations that can target chronic inflammatory processes such as fatty liver disease, cognitive impairment and aging. Welcome, Ryan. Thank you for coming. >> Good morning. Thank you for having me. >> The other distinguished panelist is Dr. Rangan Sukumar, our very own, is a distinguished technologist at the CTO office for High Performance Computing and Artificial Intelligence with a PHD in AI and 70 publications that can be applied in drug discovery, autonomous vehicles and social network analysis. Hey Rangan, welcome. Thank you for coming, by sparing the time. We have also our distinguished Chris Davidson. He is leader of our HPC and AI Application and Performance Engineering team. His job is to tune and benchmark applications, particularly in the applications of weather, energy, financial services and life sciences. Yes so particular interest is life sciences he spent 10 years in biotech and medical diagnostics. Hi Chris, welcome. Thank you for coming. >> Nice to see you. >> Well let's start with your Chris, yes, you're regularly interfaced with pharmaceutical companies and worked also on the COVID-19 White House Consortium. You know tell us, let's kick this off and tell us a little bit about your engagement in the drug discovery process. >> Right and that's a good question I think really setting the framework for what we're talking about here is to understand what is the drug discovery process. And that can be kind of broken down into I would say four different areas, there's the research and development space, the preclinical studies space, clinical trial and regulatory review. And if you're lucky, hopefully approval. Traditionally this is a slow arduous process it costs a lot of money and there's a high amount of error. Right, however this process by its very nature is highly iterate and has just huge amounts of data, right it's very data intensive, right and it's these characteristics that make this process a great target for kind of new approaches in different ways of doing things. Right, so for the sake of discussion, right, go ahead. >> Oh yes, so you mentioned data intensive brings to mind Artificial Intelligence, you know, so Artificial Intelligence making the difference here in this process, is that so? >> Right, and some of those novel approaches are actually based on Artificial Intelligence whether it's deep learning and machine learning, et cetera, you know, prime example would say, let's just say for the sake of discussion, let's say there's a brand new virus, causes flu-like symptoms, shall not be named if we focus kind of on the R and D phase, right our goal is really to identify target for the treatment and then screen compounds against it see which, you know, which ones we take forward right to this end, technologies like cryo-electron, cryogenic electron microscopy, just a form of microscopy can provide us a near atomic biomolecular map of the samples that we're studying, right whether that's a virus, a microbe, the cell that it's attaching to and so on, right AI, for instance, has been used in the particle picking aspect of this process. When you take all these images, you know, there are only certain particles that we want to take and study, right whether they have good resolution or not whether it's in the field of the frame and image recognition is a huge part of this, it's massive amounts of data in AI can be very easily, you know, used to approach that. Right, so with docking, you can take the biomolecular maps that you achieved from cryo-electron microscopy and you can take those and input that into the docking application and then run multiple iterations to figure out which will give you the best fit. AI again, right, this is iterative process it's extremely data intensive, it's an easy way to just apply AI and get that best fit doing something in a very, you know, analog manner that would just take humans very long time to do or traditional computing a very long time to do. >> Oh, Ryan, Ryan, you work at the NCNPR, you know, very exciting, you know after all, you know, at some point in history just about all drugs were from natural products yeah, so it's great to have you here today. Please tell us a little bit about your work with the pharmaceutical companies, especially when it is often that drug cocktails or what they call Polypharmacology, is the answer to complete drug therapy. Please tell us a bit more with your work there. >> Yeah thank you again for having me here this morning Dr. Goh, it's a pleasure to be here and as you said, I'm from the National Center for Natural Products Research you'll hear me refer to it as the NCNPR here in Oxford, Mississippi on the Ole Miss Campus, beautiful setting here in the South and so, what, as you said historically, what the drug discovery process has been, and it's really not a drug discovery process is really a therapy process, traditional medicine is we've looked at natural products from medicinal plants okay, in these extracts and so where I'd like to begin is really sort of talking about the assets that we have here at the NCNPR one of those prime assets, unique assets is our medicinal plant repository which comprises approximately 15,000 different medicinal plants. And what that allows us to do, right is to screen mine, that repository for activities so whether you have a disease of interest or whether you have a target of interest then you can use this medicinal plant repository to look for actives, in this case active plants. It's really important in today's environment of drug discovery to really understand what are the actives in these different medicinal plants which leads me to the second unique asset here at the NCNPR and that is our what I'll call a plant deconstruction laboratory so without going into great detail, but what that allows us to do is through a how to put workstation, right, is to facilitate rapid isolation and identification of phytochemicals in these different medicinal plants right, and so things that have historically taken us weeks and sometimes months, think acetylsalicylic acid from salicylic acid as a pain reliever in the willow bark or Taxol, right as an anti-cancer drug, right now we can do that with this system on the matter of days or weeks so now we're talking about activity from a plant and extract down to phytochemical characterization on a timescale, which starts to make sense in modern drug discovery, alright and so now if you look at these phytochemicals, right, and you ask yourself, well sort of who is interested in that and why, right what are traditional pharmaceutical companies, right which I've been working with for 20, over 25 years now, right, typically uses these natural products where historically has used these natural products as starting points for new drugs. Right, so in other words, take this phytochemical and make chemicals synthetic modifications in order to achieve a potential drug. But in the context of natural products, unlike the pharmaceutical realm, there is often times a big knowledge gap between a disease and a plant in other words I have a plant that has activity, but how to connect those dots has been really laborious time consuming so it took us probably 50 years to go from salicylic acid and willow bark to synthesize acetylsalicylic acid or aspirin it just doesn't work in today's environment. So casting about trying to figure out how we expedite that process that's when about four years ago, I read a really fascinating article in the Los Angeles Times about my colleague and business partner, Dr. Rangan Sukumar, describing all the interesting things that he was doing in the area of Artificial Intelligence. And one of my favorite parts of this story is basically, unannounced, I arrived at his doorstep in Oak Ridge, he was working Oak Ridge National Labs at the time, and I introduced myself to him didn't know what was coming, didn't know who I was, right and I said, hey, you don't know me you don't know why I'm here, I said, but let me tell you what I want to do with your system, right and so that kicked off a very fruitful collaboration and friendship over the last four years using Artificial Intelligence and it's culminated most recently in our COVID-19 project collaborative research between the NCNPR and HP in this case. >> From what I can understand also as Chris has mentioned highly iterative, especially with these combination mixture of chemicals right, in plants that could affect a disease. We need to put in effort to figure out what are the active components in that, that affects it yeah, the combination and given the layman's way of understanding it you know and therefore iterative and highly data intensive. And I can see why Rangan can play a huge significant role here, Rangan, thank you for joining us So it's just a nice segue to bring you in here, you know, given your work with Ryan over so many years now, tell I think I'm also quite interested in knowing a little about how it developed the first time you met and the process and the things you all work together on that culminated into the progress at the advanced level today. Please tell us a little bit about that history and also the current work. Rangan. >> So, Ryan, like he mentioned, walked into my office about four years ago and he was like hey, I'm working on this Omega-3 fatty acid, what can your system tell me about this Omega-3 fatty acid and I didn't even know how to spell Omega-3 fatty acids that's the disconnect between the technologist and the pharmacologist, they have terms of their own right since then we've come a long way I think I understand his terminologies now and he understands that I throw words like knowledge graphs and page rank and then all kinds of weird stuff that he's probably never heard in his life before right, so it's been on my mind off to different domains and terminologies in trying to accept each other's expertise in trying to work together on a collaborative project. I think the core of what Ryan's work and collaboration has led me to understanding is what happens with the drug discovery process, right so when we think about the discovery itself, we're looking at companies that are trying to accelerate the process to market, right an average drug is taking 12 years to get to market the process that Chris just mentioned, Right and so companies are trying to adopt what's called the in silico simulation techniques and in silico modeling techniques into what was predominantly an in vitro, in silico, in vivo environment, right. And so the in silico techniques could include things like molecular docking, could include Artificial Intelligence, could include other data-driven discovery methods and so forth, and the essential component of all the things that you know the discovery workflows have is the ability to augment human experts to do the best by assisting them with what computers do really really well. So, in terms of what we've done as examples is Ryan walks in and he's asking me a bunch of questions and few that come to mind immediately, the first few are, hey, you are an Artificial Intelligence expert can you sift through a database of molecules the 15,000 compounds that he described to prioritize a few for next lab experiments? So that's question number one. And he's come back into my office and asked me about hey, there's 30 million publications in PubMag and I don't have the time to read everything can you create an Artificial Intelligence system that once I've picked these few molecules will tell me everything about the molecule or everything about the virus, the unknown virus that shows up, right. Just trying to understand what are some ways in which he can augment his expertise, right. And then the third question, I think he described better than I'm going to was how can technology connect these dots. And typically it's not that the answer to a drug discovery problem sits in one database, right he probably has to think about uniproduct protein he has to think about phytochemical, chemical or informatics properties, data and so forth. Then he talked about the phytochemical interaction that's probably in another database. So when he is trying to answer other question and specifically in the context of an unknown virus that showed up in late last year, the question was, hey, do we know what happened in this particular virus compared to all the previous viruses? Do we know of any substructure that was studied or a different disease that's part of this unknown virus and can I use that information to go mine these databases to find out if these interactions can actually be used as a repurpose saying, hook, say this drug does not interact with this subsequence of a known virus that also seems to be part of this new virus, right? So to be able to connect that dot I think the abstraction that we are learning from working with pharma companies is that this drug discovery process is complex, it's iterative, and it's a sequence of needle in the haystack search problems, right and so one day, Ryan would be like, hey, I need to match genome, I need to match protein sequences between two different viruses. Another day it would be like, you know, I need to sift through a database of potential compounds, identified side effects and whatnot other day it could be, hey, I need to design a new molecule that never existed in the world before I'll figure out how to synthesize it later on, but I need a completely new molecule because of patentability reasons, right so it goes through the entire spectrum. And I think where HP has differentiated multiple times even the recent weeks is that the technology infusion into drug discovery, leads to several aha! Moments. And, aha moments typically happened in the other few seconds, and not the hours, days, months that Ryan has to laboriously work through. And what we've learned is pharma researchers love their aha moments and it leads to a sound valid, well founded hypothesis. Isn't that true Ryan? >> Absolutely. Absolutely. >> Yeah, at some point I would like to have a look at your, peak the list of your aha moments, yeah perhaps there's something quite interesting in there for other industries too, but we'll do it at another time. Chris, you know, with your regular work with pharmaceutical companies especially the big pharmas, right, do you see botanicals, coming, being talked about more and more there? >> Yeah, we do, right. Looking at kind of biosimilars and drugs that are already really in existence is kind of an important point and Dr. Yates and Rangan, with your work with databases this is something important to bring up and much of the drug discovery in today's world, isn't from going out and finding a brand new molecule per se. It's really looking at all the different databases, right all the different compounds that already exist and sifting through those, right of course data is mind, and it is gold essentially, right so a lot of companies don't want to share their data. A lot of those botanicals data sets are actually open to the public to use in many cases and people are wanting to have more collaborative efforts around those databases so that's really interesting to kind of see that being picked up more and more. >> Mm, well and Ryan that's where NCNPR hosts much of those datasets, yeah right and it's interesting to me, right you know, you were describing the traditional way of drug discovery where you have a target and a compound, right that can affect that target, very very specific. But from a botanical point of view, you really say for example, I have an extract from a plant that has combination of chemicals and somehow you know, it affects this disease but then you have to reverse engineer what those chemicals are and what the active ones are. Is that very much the issue, the work that has to be put in for botanicals in this area? >> Yes Doctor Goh, you hit it exactly. >> Now I can understand why a highly iterative intensive and data intensive, and perhaps that's why Rangan, you're highly valuable here, right. So tell us about the challenge, right the many to many intersection to try and find what the targets are, right given these botanicals that seem to affect the disease here what methods do you use, right in AI, to help with this? >> Fantastic question, I'm going to go a little bit deeper and speak like Ryan in terminology, but here we go. So with going back to about starting of our conversation right, so let's say we have a database of molecules on one side, and then we've got the database of potential targets in a particular, could be a virus, could be bacteria, could be whatever, a disease target that you've identified, right >> Oh this process so, for example, on a virus, you can have a number of targets on the virus itself some have the spike protein, some have the other proteins on the surface so there are about three different targets and others on a virus itself, yeah so a lot of people focus on the spike protein, right but there are other targets too on that virus, correct? >> That is exactly right. So for example, so the work that we did with Ryan we realized that, you know, COVID-19 protein sequence has an overlap, a significant overlap with previous SARS-CoV-1 virus, not only that, but it overlap with MERS, that's overlapped with some bad coronavirus that was studied before and so forth, right so knowing that and it's actually broken down into multiple and Ryan I'm going to steal your words, non-structural proteins, envelope proteins, S proteins, there's a whole substructure that you can associate an amino acid sequence with, right so on the one hand, you have different targets and again, since we did the work it's 160 different targets even on the COVID-19 mark, right and so you find a match, that we say around 36, 37 million molecules that are potentially synthesizable and try to figure it out which one of those or which few of those is actually going to be mapping to which one of these targets and actually have a mechanism of action that Ryan's looking for, that'll inhibit the symptoms on a human body, right so that's the challenge there. And so I think the techniques that we can unrule go back to how much do we know about the target and how much do we know about the molecule, alright. And if you start off a problem with I don't know anything about the molecule and I don't know anything about the target, you go with the traditional approaches of docking and molecular dynamics simulations and whatnot, right. But then, you've done so much docking before on the same database for different targets, you'll learn some new things about the ligands, the molecules that Ryan's talking about that can predict potential targets. So can you use that information of previous protein interactions or previous binding to known existing targets with some of the structures and so forth to build a model that will capture that essence of what we have learnt from the docking before? And so that's the second level of how do we infuse Artificial Intelligence. The third level, is to say okay, I can do this for a database of molecules, but then what if the protein-protein interactions are all over the literature study for millions of other viruses? How do I connect the dots across different mechanisms of actions too? Right and so this is where the knowledge graph component that Ryan was talking about comes in. So we've put together a database of about 150 billion medical facts from literature that Ryan is able to connect the dots and say okay, I'm starting with this molecule, what interactions do I know about the molecule? Is there a pretty intruding interaction that affects the mechanism of pathway for the symptoms that a disease is causing? And then he can go and figure out which protein and protein in the virus could potentially be working with this drug so that inhibiting certain activities would stop that progression of the disease from happening, right so like I said, your method of options, the options you've got is going to be, how much do you know about the target? How much do you know the drug database that you have and how much information can you leverage from previous research as you go down this pipeline, right so in that sense, I think we mix and match different methods and we've actually found that, you know mixing and matching different methods produces better synergies for people like Ryan. So. >> Well, the synergies I think is really important concept, Rangan, in additivities, synergistic, however you want to catch that. Right. But it goes back to your initial question Dr. Goh, which is this idea of polypharmacology and historically what we've done with traditional medicines there's more than one active, more than one network that's impacted, okay. You remember how I sort of put you on both ends of the spectrum which is the traditional sort of approach where we really don't know much about target ligand interaction to the completely interpretal side of it, right where now we are all, we're focused on is, in a single molecule interacting with a target. And so where I'm going with this is interesting enough, pharma has sort of migrate, started to migrate back toward the middle and what I mean by that, right, is we had these in a concept of polypharmacology, we had this idea, a regulatory pathway of so-called, fixed drug combinations. Okay, so now you start to see over the last 20 years pharmaceutical companies taking known, approved drugs and putting them in different combinations to impact different diseases. Okay. And so I think there's a really unique opportunity here for Artificial Intelligence or as Rangan has taught me, Augmented Intelligence, right to give you insight into how to combine those approved drugs to come up with unique indications. So is that patentability right, getting back to right how is it that it becomes commercially viable for entities like pharmaceutical companies but I think at the end of the day what's most interesting to me is sort of that, almost movement back toward that complex mixture of fixed drug combination as opposed to single drug entity, single target approach. I think that opens up some really neat avenues for us. As far as the expansion, the applicability of Artificial Intelligence is I'd like to talk to, briefly about one other aspect, right so what Rang and I have talked about is how do we take this concept of an active phytochemical and work backwards. In other words, let's say you identify a phytochemical from an in silico screening process, right, which was done for COVID-19 one of the first publications out of a group, Dr. Jeremy Smith's group at Oak Ridge National Lab, right, identified a natural product as one of the interesting actives, right and so it raises the question to our botanical guy, says, okay, where in nature do we find that phytochemical? What plants do I go after to try and source botanical drugs to achieve that particular end point right? And so, what Rangan's system allows us to do is to say, okay, let's take this phytochemical in this case, a phytochemical flavanone called eriodictyol and say, where else in nature is this found, right that's a trivial question for an Artificial Intelligence system. But for a guy like me left to my own devices without AI, I spend weeks combing the literature. >> Wow. So, this is brilliant I've learned something here today, right, If you find a chemical that actually, you know, affects and addresses a disease, right you can actually try and go the reverse way to figure out what botanicals can give you those chemicals as opposed to trying to synthesize them. >> Well, there's that and there's the other, I'm going to steal Rangan's thunder here, right he always teach me, Ryan, don't forget everything we talk about has properties, plants have properties, chemicals have properties, et cetera it's really understanding those properties and using those properties to make those connections, those edges, those sort of interfaces, right. And so, yes, we can take something like an eriodictyol right, that example I gave before and say, okay, now, based upon the properties of eriodictyol, tell me other phytochemicals, other flavonoid in this case, such as that phytochemical class of eriodictyols part right, now tell me how, what other phytochemicals match that profile, have the same properties. It might be more economically viable, right in other words, this particular phytochemical is found in a unique Himalayan plant that I've never been able to source, but can we find something similar or same thing growing in, you know a bush found all throughout the Southeast for example, like. >> Wow. So, Chris, on the pharmaceutical companies, right are they looking at this approach of getting, building drugs yeah, developing drugs? >> Yeah, absolutely Dr. Goh, really what Dr. Yates is talking about, right it doesn't help us if we find a plant and that plant lives on one mountain only on the North side in the Himalayas, we're never going to be able to create enough of a drug to manufacture and to provide to the masses, right assuming that the disease is widespread or affects a large enough portion of the population, right so understanding, you know, not only where is that botanical or that compound but understanding the chemical nature of the chemical interaction and the physics of it as well where which aspect affects the binding site, which aspect of the compound actually does the work, if you will and then being able to make that at scale, right. If you go to these pharmaceutical companies today, many of them look like breweries to be honest with you, it's large scale, it's large back everybody's clean room and it's, they're making the microbes do the work for them or they have these, you know, unique processes, right. So. >> So they're not brewing beer okay, but drugs instead. (Christopher laughs) >> Not quite, although there are pharmaceutical companies out there that have had a foray into the brewery business and vice versa, so. >> We should, we should visit one of those, yeah (chuckles) Right, so what's next, right? So you've described to us the process and how you develop your relationship with Dr. Yates Ryan over the years right, five years, was it? And culminating in today's, the many to many fast screening methods, yeah what would you think would be the next exciting things you would do other than letting me peek at your aha moments, right what would you say are the next exciting steps you're hoping to take? >> Thinking long term, again this is where Ryan and I are working on this long-term project about, we don't know enough about botanicals as much as we know about the synthetic molecules, right and so this is a story that's inspired from Simon Sinek's "Infinite Game" book, trying to figure it out if human population has to survive for a long time which we've done so far with natural products we are going to need natural products, right. So what can we do to help organizations like NCNPR to stage genomes of natural products to stage and understand the evolution as we go to understand the evolution to map the drugs and so forth. So the vision is huge, right so it's not something that we want to do on a one off project and go away but in the process, just like you are learning today, Dr. Goh I'm going to be learning quite a bit, having fun with life. So, Ryan what do you think? >> Ryan, we're learning from you. >> So my paternal grandfather lived to be 104 years of age. I've got a few years to get there, but back to "The Infinite Game" concept that Rang had mentioned he and I discussed that quite frequently, I'd like to throw out a vision for you that's well beyond that sort of time horizon that we have as humans, right and that's this right, is our current strategy and it's understandable is really treatment centric. In other words, we have a disease we develop a treatment for that disease. But we all recognize, whether you're a healthcare practitioner, whether you're a scientist, whether you're a business person, right or whatever occupation you realize that prevention, right the old ounce, prevention worth a pound of cure, right is how can we use something like Artificial Intelligence to develop preventive sorts of strategies that we are able to predict with time, right that's why we don't have preventive treatment approach right, we can't do a traditional clinical trial and say, did we prevent type two diabetes in an 18 year old? Well, we can't do that on a timescale that is reasonable, okay. And then the other part of that is why focus on botanicals? Is because, for the most part and there are exceptions I want to be very clear, I don't want to paint the picture that botanicals are all safe, you should just take botanicals dietary supplements and you'll be safe, right there are exceptions, but for the most part botanicals, natural products are in fact safe and have undergone testing, human testing for thousands of years, right. So how do we connect those dots? A preventive strategy with existing extent botanicals to really develop a healthcare system that becomes preventive centric as opposed to treatment centric. If I could wave a magic wand, that's the vision that I would figure out how we could achieve, right and I do think with guys like Rangan and Chris and folks like yourself, Eng Lim, that that's possible. Maybe it's in my lifetime I got 50 years to go to get to my grandfather's age, but you never know, right? >> You bring really, up two really good points there Ryan, it's really a systems approach, right understanding that things aren't just linear, right? And as you go through it, there's no impact to anything else, right taking that systems approach to understand every aspect of how things are being impacted. And then number two was really kind of the downstream, really we've been discussing the drug discovery process a lot and kind of the kind of preclinical in vitro studies and in vivo models, but once you get to the clinical trial there are many drugs that just fail, just fail miserably and the botanicals, right known to be safe, right, in many instances you can have a much higher success rate and that would be really interesting to see, you know, more of at least growing in the market. >> Well, these are very visionary statements from each of you, especially Dr. Yates, right, prevention better than cure, right, being proactive better than being reactive. Reactive is important, but we also need to focus on being proactive. Yes. Well, thank you very much, right this has been a brilliant panel with brilliant panelists, Dr. Ryan Yates, Dr. Rangan Sukumar and Chris Davidson. Thank you very much for joining us on this panel and highly illuminating conversation. Yeah. All for the future of drug discovery, that includes botanicals. Thank you very much. >> Thank you. >> Thank you.

(plane engine roars) (upbeat music) >> They call me Dr. Goh. I'm Senior Vice President and Chief Technology Officer of AI at Hewlett Packard Enterprise. And today I'm in Munich, Germany. Home to one and a half million people. Munich is famous for everything from BMW, to beer, to breathtaking architecture and festive markets. The Bavarian capital is the beating heart of Germany's automobile industry. Over 50,000 of its residents work in automotive engineering, and to date, Munich allocated around 30 million euros to boost electric vehicles and infrastructure for them. (upbeat music) >> Hello, everyone, my name is Dr. Jerome Baudry. I am a professor at the University of Alabama in Huntsville. Our mission is to use a computational resources to accelerate the discovery of drugs that will be useful and efficient against the COVID-19 virus. On the one hand, there is this terrible crisis. And on the other hand, there is this absolutely unique and rare global effort to fight it. And that I think is a is a very positive thing. I am working with the Cray HPE machine called Sentinel. This machine is so amazing that it can actually mimic the screening of hundreds of thousands, almost millions of chemicals a day. What we take weeks, if not months, or years, we can do in a matter of a few days. And it's really the key to accelerating the discovery of new drugs, new pharmaceuticals. We are all in this together, thank you. (upbeat music) >> Hello, everyone. I'm so pleased to be here to interview Dr. Jerome Baudry, of the University of Alabama in Huntsville. >> Hello, Dr. Goh, I'm very happy to be meeting with you here, today. I have a lot of questions for you as well. And I'm looking forward to this conversation between us. >> Yes, yes, and I've got lots of COVID-19 and computational science questions lined up for you too Jerome. Yeah, so let's interview each other, then. >> Absolutely, let's do that, let's interview each other. I've got many questions for you. And , we have a lot in common and yet a lot of things we are addressing from a different point of view. So I'm very much looking forward to your ideas and insights. >> Yeah, especially now, with COVID-19, many of us will have to pivot a lot of our research and development work, to address the most current issues. I watch your video and I've seen that you're very much focused on drug discovery using super computing. The central notebook you did, I'm very excited about that. Can you tell us a bit more about how that works, yeah? >> Yes, I'd be happy to in fact, I watch your video as well manufacturing, and it's actually quite surprisingly close, what we do with drugs, and with what other people do with planes or cars or assembly lanes. we are calculating forces, on molecules, on drug candidates, when they hit parts of the viruses. And we essentially try to identify what small molecules will hit the viruses or its components, the hardest to mess with its function in a way. And that's not very different from what you're doing. What you are describing people in the industry or in the transportation industry are doing. So that's our problem, so to speak, is to deal with a lot of small molecules. Guy creating a lot of forces. That's not a main problem, our main problem is to make intelligent choices about what calculates, what kind of data should we incorporate in our calculations? And what kind of data should we give to the people who are going to do the testing? And that's really something I would like you to do to help us understand better. How do you see artificial intelligence, helping us, putting our hands on the right data to start with, in order to produce the right data and accuracy. >> Yeah, that's that's a great question. And it is a question that we've been pondering in our strategy as a company a lot recently. Because more and more now we realize that the data is being generated at the far out edge. By edge. I mean, something that's outside of the cloud and data center, right? Like, for example, a more recent COVID-19 work, doing a lot of cryo electron microscope work, right? To try and get high resolution pictures of the virus and at different angles, so creating lots of movies under electron microscope to try and create a 3D model of the virus. And we realize that's the edge, right, because that's where the microscope is, away from the data center. And massive amounts of data is generated, terabytes and terabytes of data per day generated. And we had to develop means, a workflow means to get that data off the microscope and provide pre-processing and processing, so that they can achieve results without delay. So we learned quite a few lessons there, right, especially trying to get the edge to be more intelligent, to deal with the onslaught of data coming in, from these devices. >> That's fantastic that you're saying that and that you're using this very example of cryo-EM, because that's the kind of data that feeds our computations. And indeed, we have found that it is very, very difficult to get the right cryo-EM data to us. Now we've been working with HPE supercomputer Sentinel, as you may know, for our COVID-19 work. So we have a lot of computational power. But we will be even faster and better, frankly, if we knew what kind of cryo-EM data to focus on. In fact, most of our discussions are based on not so much how to compute the forces of the molecules, which we do quite well on an HP supercomputer. But again, what cryo-EM 3D dimensional space to look at. And it's becoming almost a bottleneck. >> Have access to that. >> And we spend a lot of time, do you envision a point where AI will be able to help us, to make this kind of code almost live or at least as close to live as possible, as that that comes from the edge? How to pack it and not triage it, but prioritize it for the best possible computations on supercomputers? >> What a visionary question and desire, right? Like exactly the vision we have, right? Of course, the ultimate vision, you aim for the best, and that will be a real time stream of processed data coming off the microscope straight, providing your need, right? We are not there. Before this, we are far from there, right? But that's the aim, the ability to push more and more intelligence forward, so that by the time the data reaches you, it is what you need, right, without any further processing. And a lot of AI is applied there, particularly in cryo-EM where they do particle picking, right, they do a lot of active pictures and movies of the virus. And then what they do is, they rotate the virus a little bit, right? And then to try and figure out in all the different images in the movies, to try and pick the particles in there. And this is very much image processing that AI is very good at. So many different stages, application is made. The key thing, is to deal with the data that is flowing at this at this speed, and to get the data to you in the right form, that in time. So yes, that's the desire, right? >> It will be a game changer, really. You'll be able to get things in a matter of weeks, instead of a matter of years to the colleague who will be doing the best day. If the AI can help me learn from a calculation that didn't exactly turn out the way we want it to be, that will be very, very helpful. I can see, I can envision AI being able to, live AI to be able to really revolutionize all the process, not only from the discovery, but all the way to the clinical, to the patient, to the hospital. >> Well, that's a great point. In fact, I caught on to your term live AI. That's actually what we are trying to achieve. Although I have not used that term before. Perhaps I'll borrow it for next time. >> Oh please, by all means. >> You see, yes, we have done, I've been doing also recent work on gene expression data. So a vaccine, clinical trial, they have the blood, they get the blood from the volunteers after the first day. And then to run very, very fast AI analytics on the gene expression data that the one, the transcription data, before translation to emit amino acid. The transcription data is enormous. We're talking 30,000, 60,000 different items, transcripts, and how to use that high dimensional data to predict on day one, whether this volunteer will get an adverse event or will have a good antibody outcome, right? For efficacy. So yes, how to do it so quickly, right? To get the blood, go through an SA, right, get the transcript, and then run the analytics and AI to produce an outcome. So that's exactly what we're trying to achieve, yeah. Yes, I always emphasize that, ultimately, the doctor makes that decision. Yeah, AI only suggests based on the data, this is the likely outcome based on all the previous data that the machine has learned from, yeah. >> Oh, I agree, we wouldn't want the machine to decide the fate of the patient, but to assist the doctor or nurse making the decision that will be invaluable? And are you aware of any kind of industry that already is using this kind of live AI? And then, is there anything in, I don't know in sport or crowd control? Or is there any kind of industry? I will be curious to see who is ahead of us in terms of making this kind of a minute based decisions using AI? Yes, in fact, this is very pertinent question. We as In fact, COVID-19, lots of effort working on it, right? But now, industries and different countries are starting to work on returning to work, right, returning to their offices, returning to the factories, returning to the manufacturing plants, but yet, the employers need to reassure the employees that things, appropriate measures are taken for safety, but yet maintain privacy, right? So our Aruba organization actually developed a solution called contact location tracing inside buildings, inside factories, right? Why they built this, and needed a lot of machine learning methods in there to do very, very well, as you say, live AI right? To offer a solution? Well, let me describe the problem. The problem is, in certain countries, and certain states, certain cities where regulations require that, if someone is ill, right, you actually have to go in and disinfect the area person has been to, is a requirement. But if you don't know precisely where the ill person has been to, you actually disinfect the whole factory. And if you have that, if you do that, it becomes impractical and cost prohibitive for the company to keep operating profitably. So what they are doing today with Aruba is, that they carry this Bluetooth Low Energy tag, which is a quarter size, right? The reason they do that is, so that they extract the tag from the person, and then the system tracks, everybody, all the employees. We have one company, there's 10,000 employees, right? Tracks everybody with the tag. And if there is a person ill, immediately a floor plan is brought up with hotspots. And then you just targeted the cleaning services there. The same thing, contact tracing is also produced automatically, you could say, anybody that is come in contact with this person within two meters, and more than 15 minutes, right? It comes up the list. And we, privacy is our focused here. There's a separation between the tech and the person, on only restricted people are allowed to see the association. And then things like washrooms and all that are not tracked here. So yes, live AI, trying to make very, very quick decisions, right, because this affects people. >> Another question I have for you, if you have a minute, actually has to be the same thing. Though, it's more a question about hardware, about computer hardware purify may. We're having, we're spending a lot of time computing on number crunching giant machines, like Sentinel, for instance, which is a dream to use, but it's very good at something but when we pulled it off, also spent a lot of time moving back and forth, so data from clouds from storage, from AI processing, to the computing cycles back and forth, back and forth, did you envision an architecture, that will kind of, combine the hardware needed for a massively parallel calculations, kind of we are doing. And also very large storage, fast IO to be more AI friendly, so to speak. You see on the horizon, some kind of, I would say you need some machine, maybe it's to be determined, to be ambitious at times but something that, when the AI ahead plan in terms of passing the vector to the massively parallel side, yeah, that makes sense? >> Makes a lot of sense. And you ask it I know, because it is a tough problem to solve, as we always say, computation, right, is growing capability enormously. But bandwidth, you have to pay for, latency you sweat for, right? >> That's a very good >> So moving data is ultimately going to be the problem. >> It is. >> Yeah, and we've move the data a lot of times, right, >> You move back and forth, so many times >> Back and forth, back and forth, from the edge that's where you try to pre-process it, before you put it in storage, yeah. But then once it arrives in storage, you move it to memory to do some work and bring it back and move it memory again, right, and then that's what HPC, and then you put it back into storage, and then the AI comes in you, you do the learning, the other way around also. So lots of back and forth, right. So tough problem to solve. But more and more, we are looking at a new architecture, right? Currently, this architecture was built for the AI side first, but we're now looking and see how we can expand that. And this is that's the reason why we announced HPE Ezmeral Data Fabric. What it does is that, it takes care of the data, all the way from the edge point of view, the minute it is ingested at the edge, it is incorporated in the global namespace. So that eventually where the data arrives, lands at geographically one, or lands at, temperature, hot data, warm data or cold data, regardless of eventually where it lands at, this Data Fabric checks everything, from in a global namespace, in a unified way. So that's the first step. So that data is not seen as in different places, different pieces, it is a unified view of all the data, the minute that it does, Just start from the edge. >> I think it's important that we communicate that AI is purposed for good, A lot of sci-fi movies, unfortunately, showcase some psychotic computers or teams of evil scientists who want to take over the world. But how can we communicate better that it's a tool for a change, a tool for good? >> So key differences are I always point out is that, at least we have still judgment relative to the machine. And part of the reason we still have judgment is because our brain, logical center is automatically connected to our emotional center. So whatever our logic say is tempered by emotion, and whatever our emotion wants to act, wants to do, right, is tempered by our logic, right? But then AI machine is, many call them, artificial specific intelligence. They are just focused on that decision making and are not connected to other more culturally sensitive or emotionally sensitive type networks. They are focus networks. Although there are people trying to build them, right. That's this power, reason why with judgment, I always use the phrase, right, what's correct, is not always the right thing to do. There is a difference, right? We need to be there to be the last Judge of what's right, right? >> Yeah. >> So that says one of the the big thing, the other one, I bring up is that humans are different from machines, generally, in a sense that, we are highly subtractive. We, filter, right? Well, machine is highly accumulative today. So an AI machine they accumulate to bring in lots of data and tune the network, but our brains a few people realize, we've been working with brain researchers in our work, right? Between three and 30 years old, our brain actually goes through a pruning process of our connections. So for those of us like me after 30 it's done right. (laughs) >> Wait till you reach my age. >> Keep the brain active, because it prunes away connections you don't use, to try and conserve energy, right? I always say, remind our engineers about this point, about prunings because of energy efficiency, right? A slice of pizza drives our brain for three hours. (laughs) That's why, sometimes when I get need to get my engineers to work longer, I just offer them pizza, three more hours, >> Pizza is universal solution to our problems, absolutely. Food Indeed, indeed. There is always a need for a human consciousness. It's not just a logic, it's not like Mr. Spock in "Star Trek," who always speaks about logic but forgets the humanity aspect of it. >> Yes, yes, The connection between the the logic centers and emotional centers, >> You said it very well. Yeah, yeah and the thing is, sleep researchers are saying that when you don't get enough REM sleep, this connection is weakened. Therefore, therefore your decision making gets affected if you don't get enough sleep. So I was thinking, people do alcohol test breathalyzer test before they are allowed to operate sensitive or make sensitive decisions. Perhaps in the future, you have to check whether you have enough REM sleep before, >> It is. This COVID-19 crisis obviously problematic, and I wish it never happened, but there is something that I never experienced before is, how people are talking to each other, people like you and me, we have a lot in common. But I hear more about the industry outside of my field. And I talk a lot to people, like cryo-EM people or gene expression people, I would have gotten the data before and process it. Now, we have a dialogue across the board in all aspects of industry, science, and society. And I think that could be something wonderful that we should keep after we finally fix this bug. >> Yes. yes, yes. >> Right? >> Yes, that's that's a great point. In fact, it's something I've been thinking about, right, for employees, things have changed, because of COVID-19. But very likely, the change will continue, yeah? >> Right. Yes, yes, because there are a few positive outcomes. COVID-19 is a tough outcome. But there positive side of things, like communicating in this way, effectively. So we were part of the consortium that developed a natural language processing system in AI system that would allow you scientists to do, I can say, with the link to that website, allows you to do a query. So say, tell me the latest on the binding energy between the Sasko B2 virus like protein and the AC receptor. And then you will, it will give you a list of 10 answers, yeah? And give you a link to the papers that say, they say those answers. If you key that in today to NLP, you see 315 points -13.7 kcal per mole, which is right, I think the general consensus answer, and see a few that are highly out of out of range, right? And then when you go further, you realize those are the earlier papers. So I think this NLP system will be useful. (both chattering) I'm sorry, I didn't mean to interrupt, but I mentioned yesterday about it, because I have used that, and it's a game changer indeed, it is amazing, indeed. Many times by using this kind of intelligent conceptual, analyzes a very direct use, that indeed you guys are developing, I have found connections between facts, between clinical or pharmaceutical aspects of COVID-19. That I wasn't really aware of. So a it's a tool for creativity as well, I find it, it builds something. It just doesn't analyze what has been done, but it creates the connections, it creates a network of knowledge and intelligence. >> That's why three to 30 years old, when it stops pruning. >> I know, I know. (laughs) But our children are amazing, in that respect, they see things that we don't see anymore. they make connections that we don't necessarily think of, because we're used to seeing a certain way. And the eyes of a child, are bringing always something new, which I think is what AI could potentially bring here. So look, this is fascinating, really. >> Yes, yes, difference between filtering subtractive and the machine being accumulative. That's why I believe, the two working together, can have a stronger outcome if used properly. >> Absolutely. And I think that's how AI will be a force for good indeed. Obviously see, seems that we would have missed that would end up being very important. Well, we are very interested in or in our quest for drug discovery against COVID-19, we have been quite successful so far. We have accelerated the process by an order of magnitude. So we're having molecules that are being tested against the virus, otherwise, it would have taken maybe three or four years to get to that point. So first thing, we have been very fast. But we are very interested in natural products, that chemicals that come from plants, essentially. We found a way to mine, I don't want to say explore it, but leverage, that knowledge of hundreds of years of people documenting in a very historical way of what plants do against what diseases in different parts of the world. So that really has been a, not only very useful in our work, but a fantastic bridge to our common human history, basically. And second, yes, plants have chemicals. And of course we love chemicals. Every living cell has chemicals. The chemicals that are in plants, have been fine tuned by evolution to actually have some biological function. They are not there just to look good. They have a role in the cell. And if we're trying to come up with a new growth from scratch, which is also something we want to do, of course, then we have to engineer a function that evolution hasn't already found a solution to, for in plants, so in a way, it's also artificial intelligence. We have natural solutions to our problems, why don't we try to find them and see their work in ourselves, we're going to, and this is certainly have to reinvent the wheel each time. >> Hundreds of millions of years of evolution, >> Hundreds of millions of years. >> Many iterations, >> Yes, ending millions of different plants with all kinds of chemical diversity. So we have a lot of that, at our disposal here. If only we find the right way to analyze them, and bring them to our supercomputers, then we will, we will really leverage this humongus amount of knowledge. Instead of having to reinvent the wheel each time we want to take a car, we'll find that there are cars whose wheels already that we should be borrowing instead of, building one each time. Most of the keys are out there, if we can find them, They' re at our disposal. >> Yeah, nature has done the work after hundreds of millions of years. >> Yes. (chattering) Is to figure out, which is it, yeah? Exactly, exactly hence the importance of biodiversity. >> Yeah, I think this is related to the Knowledge Graph, right? Where, yes, to objects and the linking parameter, right? And then you have hundreds of millions of these right? A chemical to an outcome and the link to it, right? >> Yes, that's exactly what it is, absolutely the kind of things we're pursuing very much, so absolutely. >> Not only only building the graph, but building the dynamics of the graph, In the future, if you eat too much Creme Brulee, or if you don't run enough, or if you sleep, well, then your cells, will have different connections on this graph of the ages, will interact with that molecule in a different way than if you had more sleep or didn't eat that much Creme Brulee or exercise a bit more, >> So insightful, Dr. Baudry. Your, span of knowledge, right, impressed me. And it's such fascinating talking to you. (chattering) Hopefully next time, when we get together, we'll have a bit of Creme Brulee together. >> Yes, let's find out scientifically what it does, we have to do double blind and try three times to make sure we get the right statistics. >> Three phases, three clinical trial phases, right? >> It's been a pleasure talking to you. I like we agreed, you knows this, for all that COVID-19 problems, the way that people talk to each other is, I think the things that I want to keep in this in our post COVID-19 world. I appreciate very much your insight and it's very encouraging the way you see things. So let's make it happen. >> We will work together Dr.Baudry, hope to see you soon, in person. >> Indeed in person, yes. Thank you. >> Thank you, good talking to you.

>>welcome. My name is Ben Bennett. I am the director of HPC Strategic programs here at Hewlett Packard Enterprise. It is my great pleasure and honor to be talking to Professor Mark Parsons from the Edinburgh Parallel Computing Center. And we're gonna talk a little about exa scale. What? It means we're gonna talk less about the technology on Maura about the science, the requirements on the need for exa scale. Uh, rather than a deep dive into the enabling technologies. Mark. Welcome. >>I then thanks very much for inviting me to tell me >>complete pleasure. Um, so I'd like to kick off with, I suppose. Quite an interesting look back. You and I are both of a certain age 25 plus, Onda. We've seen these milestones. Uh, I suppose that the S I milestones of high performance computing's come and go, you know, from a gig a flop back in 1987 teraflop in 97 a petaflop in 2000 and eight. But we seem to be taking longer in getting to an ex a flop. Um, so I'd like your thoughts. Why is why is an extra flop taking so long? >>So I think that's a very interesting question because I started my career in parallel computing in 1989. I'm gonna join in. IPCC was set up then. You know, we're 30 years old this year in 1990 on Do you know the fastest computer we have them is 800 mega flops just under a getting flogged. So in my career, we've gone already. When we reached the better scale, we'd already gone pretty much a million times faster on, you know, the step from a tariff block to a block scale system really didn't feel particularly difficult. Um, on yet the step from A from a petaflop PETA scale system. To an extent, block is a really, really big challenge. And I think it's really actually related to what's happened with computer processes over the last decade, where, individually, you know, approached the core, Like on your laptop. Whoever hasn't got much faster, we've just got more often So the perception of more speed, but actually just being delivered by more course. And as you go down that approach, you know what happens in the supercomputing world as well. We've gone, uh, in 2010 I think we had systems that were, you know, a few 1000 cores. Our main national service in the UK for the last eight years has had 118,000 cores. But looking at the X scale we're looking at, you know, four or five million cores on taming that level of parallelism is the real challenge. And that's why it's taking an enormous and time to, uh, deliver these systems. That is not just on the hardware front. You know, vendors like HP have to deliver world beating technology and it's hard, hard. But then there's also the challenge to the users. How do they get the codes to work in the face of that much parallelism? >>If you look at what the the complexity is delivering an annex a flop. Andi, you could have bought an extra flop three or four years ago. You couldn't have housed it. You couldn't have powered it. You couldn't have afforded it on, do you? Couldn't program it. But you still you could have You could have bought one. We should have been so lucky to be unable to supply it. Um, the software, um I think from our standpoint, is is looking like where we're doing mawr enabling with our customers. You sell them a machine on, then the the need then to do collaboration specifically seems mawr and Maura around the software. Um, so it's It's gonna be relatively easy to get one x a flop using limb pack, but but that's not extra scale. So what do you think? On exa scale machine versus an X? A flop machine means to the people like yourself to your users, the scientists and industry. What is an ex? A flop versus >>an exa scale? So I think, you know, supercomputing moves forward by setting itself challenges. And when you when you look at all of the excess scale programs worldwide that are trying to deliver systems that can do an X a lot form or it's actually very arbitrary challenge. You know, we set ourselves a PETA scale challenge delivering a petaflop somebody manage that, Andi. But you know, the world moves forward by setting itself challenges e think you know, we use quite arbitrary definition of what we mean is well by an exit block. So, you know, in your in my world, um, we either way, first of all, see ah flop is a computation, so multiply or it's an ad or whatever on we tend. Thio, look at that is using very high precision numbers or 64 bit numbers on Do you know, we then say, Well, you've got to do the next block. You've got to do a billion billion of those calculations every second. No, a some of the last arbitrary target Now you know today from HPD Aiken by my assistant and will do a billion billion calculations per second. And they will either do that as a theoretical peak, which would be almost unattainable, or using benchmarks that stressed the system on demonstrate a relaxing law. But again, those benchmarks themselves attuned Thio. Just do those calculations and deliver and explore been a steady I'll way if you like. So, you know, way kind of set ourselves this this this big challenge You know, the big fence on the race course, which were clambering over. But the challenge in itself actually should be. I'm much more interesting. The water we're going to use these devices for having built um, eso. Getting into the extra scale era is not so much about doing an extra block. It's a new generation off capability that allows us to do better scientific and industrial research. And that's the interesting bit in this whole story. >>I would tend to agree with you. I think the the focus around exa scale is to look at, you know, new technologies, new ways of doing things, new ways of looking at data and to get new results. So eventually you will get yourself a nexus scale machine. Um, one hopes, sooner rather >>than later. Well, I'm sure you don't tell me one, Ben. >>It's got nothing to do with may. I can't sell you anything, Mark. But there are people outside the door over there who would love to sell you one. Yes. However, if we if you look at your you know your your exa scale machine, Um, how do you believe the workloads are going to be different on an extra scale machine versus your current PETA scale machine? >>So I think there's always a slight conceit when you buy a new national supercomputer. On that conceit is that you're buying a capability that you know on. But many people will run on the whole system. Known truth. We do have people that run on the whole of our archer system. Today's A 118,000 cores, but I would say, and I'm looking at the system. People that run over say, half of that can be counted on Europe on a single hand in a year, and they're doing very specific things. It's very costly simulation they're running on. So, you know, if you look at these systems today, two things show no one is. It's very difficult to get time on them. The Baroque application procedures All of the requirements have to be assessed by your peers and your given quite limited amount of time that you have to eke out to do science. Andi people tend to run their applications in the sweet spot where their application delivers the best performance on You know, we try to push our users over time. Thio use reasonably sized jobs. I think our average job says about 20,000 course, she's not bad, but that does mean that as we move to the exits, kill two things have to happen. One is actually I think we've got to be more relaxed about giving people access to the system, So let's give more people access, let people play, let people try out ideas they've never tried out before. And I think that will lead to a lot more innovation and computational science. But at the same time, I think we also need to be less precious. You know, we to accept these systems will have a variety of sizes of job on them. You know, we're still gonna have people that want to run four million cores or two million cores. That's absolutely fine. Absolutely. Salute those people for trying really, really difficult. But then we're gonna have a huge spectrum of views all the way down to people that want to run on 500 cores or whatever. So I think we need Thio broaden the user base in Alexa Skill system. And I know this is what's happening, for example, in Japan with the new Japanese system. >>So, Mark, if you cast your mind back to almost exactly a year ago after the HPC user forum, you were interviewed for Premier Magazine on Do you alluded in that article to the needs off scientific industrial users requiring, you know, uh on X a flop or an exa scale machine it's clear in your in your previous answer regarding, you know, the workloads. Some would say that the majority of people would be happier with, say, 10 100 petaflop machines. You know, democratization. More people access. But can you provide us examples at the type of science? The needs of industrial users that actually do require those resources to be put >>together as an exa scale machine? So I think you know, it's a very interesting area. At the end of the day, these systems air bought because they are capability systems on. I absolutely take the argument. Why shouldn't we buy 10 100 pattern block systems? But there are a number of scientific areas even today that would benefit from a nexus school system and on these the sort of scientific areas that will use as much access onto a system as much time and as much scale of the system as they can, as you can give them eso on immediate example. People doing chroma dynamics calculations in particle physics, theoretical calculations, they would just use whatever you give them. But you know, I think one of the areas that is very interesting is actually the engineering space where, you know, many people worry the engineering applications over the last decade haven't really kept up with this sort of supercomputers that we have. I'm leading a project called Asimov, funded by M. P S O. C in the UK, which is jointly with Rolls Royce, jointly funded by Rolls Royce and also working with the University of Cambridge, Oxford, Bristol, Warrick. We're trying to do the whole engine gas turbine simulation for the first time. So that's looking at the structure of the gas turbine, the airplane engine, the structure of it, how it's all built it together, looking at the fluid dynamics off the air and the hot gasses, the flu threat, looking at the combustion of the engine looking how fuel is spread into the combustion chamber. Looking at the electrics around, looking at the way the engine two forms is, it heats up and cools down all of that. Now Rolls Royce wants to do that for 20 years. Andi, Uh, whenever they certify, a new engine has to go through a number of physical tests, and every time they do on those tests, it could cost them as much as 25 to $30 million. These are very expensive tests, particularly when they do what's called a blade off test, which would be, you know, blade failure. They could prove that the engine contains the fragments of the blade. Sort of think, continue face really important test and all engines and pass it. What we want to do is do is use an exa scale computer to properly model a blade off test for the first time, so that in future, some simulations can become virtual rather than having thio expend all of the money that Rolls Royce would normally spend on. You know, it's a fascinating project is a really hard project to do. One of the things that I do is I am deaf to share this year. Gordon Bell Price on bond I've really enjoyed to do. That's one of the major prizes in our area, you know, gets announced supercomputing every year. So I have the pleasure of reading all the submissions each year. I what's been really interesting thing? This is my third year doing being on the committee on what's really interesting is the way that big systems like Summit, for example, in the US have pushed the user communities to try and do simulations Nowhere. Nobody's done before, you know. And we've seen this as well, with papers coming after the first use of the for Goku system in Japan, for example, people you know, these are very, very broad. So, you know, earthquake simulation, a large Eddie simulations of boats. You know, a number of things around Genome Wide Association studies, for example. So the use of these computers spans of last area off computational science. I think the really really important thing about these systems is their challenging people that do calculations they've never done before. That's what's important. >>Okay, Thank you. You talked about challenges when I nearly said when you and I had lots of hair, but that's probably much more true of May. Um, we used to talk about grand challenges we talked about, especially around the teraflop era, the ski red program driving, you know, the grand challenges of science, possibly to hide the fact that it was a bomb designing computer eso they talked about the grand challenges. Um, we don't seem to talk about that much. We talk about excess girl. We talk about data. Um Where are the grand challenges that you see that an exa scale computer can you know it can help us. Okay, >>so I think grand challenges didn't go away. Just the phrase went out of fashion. Um, that's like my hair. I think it's interesting. The I do feel the science moves forward by setting itself grand challenges and always had has done, you know, my original backgrounds in particle physics. I was very lucky to spend four years at CERN working in the early stage of the left accelerator when it first came online on. Do you know the scientists there? I think they worked on left 15 years before I came in and did my little ph d on it. Andi, I think that way of organizing science hasn't changed. We just talked less about grand challenges. I think you know what I've seen over the last few years is a renaissance in computational science, looking at things that have previously, you know, people have said have been impossible. So a couple of years ago, for example, one of the key Gordon Bell price papers was on Genome Wide Association studies on some of it. If I may be one of the winner of its, if I remember right on. But that was really, really interesting because first of all, you know, the sort of the Genome Wide Association Studies had gone out of favor in the bioinformatics by a scientist community because people thought they weren't possible to compute. But that particular paper should Yes, you could do these really, really big Continental little problems in a reasonable amount of time if you had a big enough computer. And one thing I felt all the way through my career actually is we've probably discarded Mawr simulations because they were impossible at the time that we've actually decided to do. And I sometimes think we to challenge ourselves by looking at the things we've discovered in the past and say, Oh, look, you know, we could actually do that now, Andi, I think part of the the challenge of bringing an extra service toe life is to get people to think about what they would use it for. That's a key thing. Otherwise, I always say, a computer that is unused to just be turned off. There's no point in having underutilized supercomputer. Everybody loses from that. >>So Let's let's bring ourselves slightly more up to date. We're in the middle of a global pandemic. Uh, on board one of the things in our industry has bean that I've been particularly proud about is I've seen the vendors, all the vendors, you know, offering up machine's onboard, uh, making resources available for people to fight things current disease. Um, how do you see supercomputers now and in the future? Speeding up things like vaccine discovery on help when helping doctors generally. >>So I think you're quite right that, you know, the supercomputer community around the world actually did a really good job of responding to over 19. Inasmuch as you know, speaking for the UK, we put in place a rapid access program. So anybody wanted to do covert research on the various national services we have done to the to two services Could get really quick access. Um, on that, that has worked really well in the UK You know, we didn't have an archer is an old system, Aziz. You know, we didn't have the world's largest supercomputer, but it is happily bean running lots off covert 19 simulations largely for the biomedical community. Looking at Druk modeling and molecular modeling. Largely that's just been going the US They've been doing really large uh, combinatorial parameter search problems on on Summit, for example, looking to see whether or not old drugs could be reused to solve a new problem on DSO, I think, I think actually, in some respects Kobe, 19 is being the sounds wrong. But it's actually been good for supercomputing. Inasmuch is pointed out to governments that supercomputers are important parts off any scientific, the active countries research infrastructure. >>So, um, I'll finish up and tap into your inner geek. Um, there's a lot of technologies that are being banded around to currently enable, you know, the first exa scale machine, wherever that's going to be from whomever, what are the current technologies or emerging technologies that you are interested in excited about looking forward to getting your hands on. >>So in the business case I've written for the U. K's exa scale computer, I actually characterized this is a choice between the American model in the Japanese model. Okay, both of frozen, both of condoms. Eso in America, they're very much gone down the chorus plus GPU or GPU fruit. Um, so you might have, you know, an Intel Xeon or an M D process er center or unarmed process or, for that matter on you might have, you know, 24 g. P. U s. I think the most interesting thing that I've seen is definitely this move to a single address space. So the data that you have will be accessible, but the G p u on the CPU, I think you know, that's really bean. One of the key things that stopped the uptake of GPS today and that that that one single change is going Thio, I think, uh, make things very, very interesting. But I'm not entirely convinced that the CPU GPU model because I think that it's very difficult to get all the all the performance set of the GPU. You know, it will do well in H p l, for example, high performance impact benchmark we're discussing at the beginning of this interview. But in riel scientific workloads, you know, you still find it difficult to find all the performance that has promised. So, you know, the Japanese approach, which is the core, is only approach. E think it's very attractive, inasmuch as you know They're using very high bandwidth memory, very interesting process of which they are going to have to, you know, which they could develop together over 10 year period. And this is one thing that people don't realize the Japanese program and the American Mexico program has been working for 10 years on these systems. I think the Japanese process really interesting because, um, it when you look at the performance, it really does work for their scientific work clothes, and that's that does interest me a lot. This this combination of a A process are designed to do good science, high bandwidth memory and a real understanding of how data flows around the supercomputer. I think those are the things are exciting me at the moment. Obviously, you know, there's new networking technologies, I think, in the fullness of time, not necessarily for the first systems. You know, over the next decade we're going to see much, much more activity on silicon photonics. I think that's really, really fascinating all of these things. I think in some respects the last decade has just bean quite incremental improvements. But I think we're supercomputing is going in the moment. We're a very very disruptive moment again. That goes back to start this discussion. Why is extra skill been difficult to get? Thio? Actually, because the disruptive moment in technology. >>Professor Parsons, thank you very much for your time and your insights. Thank you. Pleasure and folks. Thank you for watching. I hope you've learned something, or at least enjoyed it. With that, I would ask you to stay safe and goodbye.

>> Hi everyone, this is Dave Vellante and I want to welcome you to our celebration of Exascale Day. A community event with support from Hewlett Packard Enterprise. Now, Exascale Day is October 18th, that's 10, 18 as in 10 to the power of 18. And on that day we celebrate the scientists, and researchers, who make breakthrough discoveries, with the assistance, of some of the most sophisticated supercomputers in the world. Ones that can run and Exascale. Now in this program, we're going to kick off the weekend and discuss the significance of Exascale computing, how we got here, why it's so challenging to get to the point where we're at now where we can perform almost, 10 to the 18th floating point operations per second. Or an exaFLOP. We should be there by 2021. And importantly, what innovations and possibilities Exascale computing will unlock. So today, we got a great program for you. We're not only going to dig into a bit of the history of supercomputing, we're going to talk with experts, folks like Dr. Ben Bennett, who's doing and some work with the UK government. And he's going to talk about some of the breakthroughs that we can expect with Exascale. You'll also hear from experts like, Professor Mark Parsons of the University of Edinburgh, who cut his teeth at CERN, in Geneva. And Dr. Brian Pigeon Nuskey of Purdue University, who's studying buyer diversity. We're going to also hear about supercomputers in space as we get as a great action going on with supercomputers up at the International Space Station. Let me think about that, powerful high performance water-cooled supercomputers, running on solar, and mounted overhead, that's right. Even though at the altitude at the International Space Station, there's 90% of the Earth's gravity. Objects, including humans they're essentially in a state of free fall. At 400 kilometers above earth, there no air. You're in a vacuum. Like have you ever been on the Tower of Terror at Disney? In that free fall ride, or a nosedive in an airplane, I have. And if you have binoculars around your neck, they would float. So the supercomputers can actually go into the ceiling, crazy right? And that's not all. We're going to hear from experts on what the exascale era. will usher in for not only space exploration, but things like weather forecasting, life sciences, complex modeling, and all types of scientific endeavors. So stay right there for all the great content. You can use the #ExascaleDay on Twitter, and, enjoy the program. Thanks everybody for watching.

>> Narrator: From around the globe. It's theCUBE. With digital coverage of Dell Technologies World-Digital Experience. Brought to you by Dell Technologies. >> Hello everyone, and welcome back to theCUBE's continuing coverage of Dell Tech World 2020. This is Dave Vellante, and I'm here with Sam Groccot. Who's the Senior Vice President of Product Marketing at Dell Technologies. Sam, great to see you. Welcome. >> Great to be here, Dave. >> All right, we're going to talk generally about cloud in the coming decade. And really how the cloud model is evolving. But I want to specifically ask Sam about the as a service news that Dell's making at DTW. What those solutions look like. How they're going to evolve. Maybe Sam, we can hit on some of the customer uptake and the feedback as well. Does that sound good? >> Yeah, sounds great. Let's dive right in. >> All right, let's do that. So look, you've come from the world of disrupter. When you joined Isilon, they got acquired by EMC and then Dell. So, you've been on both sides of the competitive table. And cloud is obviously a major force actually I'd say the major disruptive force in our industry. Let's talk about how Dell is responding to the cloud trend generally. Then we'll get into the announcements. >> Yeah, certainly. And you're right I've been on both sides of this. There is no doubt if you look at just over the last decade or so. How customers and partners are really looking at evaluating how they can take advantage of the value of moving workloads to the cloud. And we've seen it happen over the last decade or so. And it's happening at a more frequent pace. There's no doubt that is really what planted the seed of this new operating experience. Kind of a new lifestyle so to speak, around as a service. Because when you go to the cloud, that's the only way they roll. Is you get an as a service experience. So, that really has started to come into the data center. As organizations are moving specific workloads or applications to the cloud. Of hey, how do I get that in an on-premise experience? I think throwing gasoline on that is certainly the pandemic and COVID-19. Has really made organizations evaluate how to move much quicker and more agilely by moving some applications to the cloud. Because frankly on-prem just wasn't able to move as fast as they'd like to see. We're seeing that macrotrend accelerate. I think we're in good shape to take advantage of that as we go forward. >> Well, that brings us to the hard news of what you're calling Project Apex i.e your as a service initiative. What specifically are you announcing this week? >> Yeah. So, Project Apex is one of our big announcements and that's really where we're targeting. How we're bringing together and unifying our product development. Our sales go-to-market. Our marketing go-to-market. Everything coming together underneath Project Apex. Which is our as a service and cloud like experience. Look, we know in that world where customers we're constantly evaluating which applications stay on-prem. Which applications and workloads should go to the cloud. I think the market has certainly voted clearly that it's going to be both. It's going to be a hybrid multicloud world. But what they absolutely are clear that they want is a simple, easy to use as a service experience. Regardless of if they're on-prem or off-prem. And that's where really the traditional on-prem solutions fall down. Because it's just too darn complex still. They've got many different tools, managing many different applications that oversee their cloud operations, their various infrastructure, whether it's server or compute or networking. They all run different tools. So, it gets very, very complex. It also very rigid to scale. You can't move as fast as the cloud. It can't deploy as fast. It requires manual intervention to buy more. You typically got to get a sales rep in-house to come in and extend your environment and grow your environment. And then of course, the traditional method is very CapEx heavy. In a world where organizations are really trying to preserve cash. Cash is king. It doesn't really give them the flexibility traditionally or going forward that they'd like to see on that front. So, what they want to see is a consistent operating experience for their on and off-prem environments. They want to see a single tool that can manage, report and grow and do commerce across that environment. Regardless of if it's on or off-prem. They want something that can scale quickly. Now look, when you're moving equipment on-prem, it's not going to be a click of a button. But you should be able to buy and procure that with a click of a button. And then very quickly, within less than a handful of days. That equipment should be stood up deployed and running in their environment. And then finally, it's got to deliver this more flexible finance model. Whether it's leveraging a flexible subscription models or OPEX friendly models. Customers are really looking for that more OPEX friendly approach. Which we're going to be providing with Project Apex. So very, very excited about kind of the goals and the aspirations of Project Apex. We're going to see a lot of it come to market early next year. I think we're well situated, as I said, to take advantage of this opportunity. >> So, when I was looking through the announcement and sort of squinting through it. The three things jumped out and you've definitely hit on those. One is choice. But sometimes you don't want to give customers too much choice. So, it's got to be simple and it's got to be consistent. So, it feels like you're putting this abstraction layer over your entire portfolio and trying to hit on those three items. Which is somewhat of a balancing act. Is that right? >> Yeah. No, you're exactly right. The kind of the pillars of the Project Apex value proposition so to speak, is simplicity choice and consistency. So, we've got to deliver that simple kind of end to end journey view of their entire cloud and as a service experience. It needs to span our entire portfolio. So, whether it's servers, storage or networking or PCs or cloud. All of that needs to be integrated into essentially a large, single web interface that gives you visibility across all of that. And of course, the ease of scale up and frankly scaled down. Should be able to do that in real time through the system. Choice is a big, big factor for us. We've got the broadest portfolio in the industry. We want to provide customers the ability to consume infrastructure any way they want. Clearly they can consume it the traditional way. But this more as a service flexible consumption approach is fundamental to making sure customers only pay for what they use. So, highly metered environment. Pay as they go. You leverage subscriptions. Essentially give them that OPEX flexibility that they've been looking for. And then finally, I think the real key differentiator is that consistent operating experience. So, whether you move workloads on or off-prem. It's got to be in a single environment that doesn't require you to jump around between different application and management experiences. >> Alright, so I've got to ask you the tough question. I want to hear your answer to it. I mean, we've seen the cloud model. Everybody knows it very well. But why now? People are going to say, okay, you're just responding to HPE. What's different between what you're doing and what some of your competitors are doing? >> Yeah. So, I think it really comes down to the choice and breadth of what we're bringing to the table. So, we're not going to force our customers to go down one of these routes. We're going to provide that ultimate flexibility. And I think what will really define ourselves against them and shine ourselves against them is, that consistent operating experience. We've got that opportunity to provide both an on-prem, Edge and cloud experience. That doesn't require them to move out of that operating experience to jump between different tools. So, whether you're running a Storage as a service environment. Which we'll have in the first half of next year. Looking through our new cloud console that is coming out early next year as well. You're going to be able to have that single view of everything that's going on across your environment. And also be able to move workloads from on-prem and off-prem without breaking that consistent experience. I think that is probably the biggest differentiator we're going to have. When you ladder that onto just the general Dell Technologies value of being able to meet and deliver our solutions anywhere in the world at any point of the data center, at the Edge, or even cloud-native. We've got the broadest portfolio to meet our customer needs wherever we need to go. >> So, my understanding is the offerings, it's designed to encompass the entire Dell Technologies portfolio. >> That's right. >> From client solutions, ISG, et cetera. Not VMware specifically. It's really that whole Dell Technologies portfolio. Correct? >> Yeah and look, over time we totally expect to be able to transact to VMware through this. We do expect that to be part of the solution eventually. So yeah, it is across, PC as a service, Storage as a service, Infrastructure as a service. Our cloud offers all of our services, traditional services that are helping to deliver this as a service experience. And even our traditional financial flexible consumption models will be included in this. Because again, we want to offer ultimate choice and flexibility. We're not going to force our customers to go down any of these paths. But what we want to do is present these paths and go wherever they want to go. We've got the breadth of the portfolio and the offers to get them there. >> Oh, okay. So, it's really a journey. You mentioned Storage as a service coming out first and then as well. If I understand it, the idea is to, I'm going to have visibility and control over my entire state on-prem, cloud, Edge, kind of the whole enchilada. Maybe not right out of the shoot, but that's the vision. >> Absolutely. You've got to be able to see all of that and we'll continue to iterate over time and bring more environments, more applications, more cloud environments into this. But that is absolutely the vision of Project Apex is to deliver that fully integrated core, Edge, cloud partner experience. To all of the environments our customers could be running in. >> I want to put my customer hat on my CFO, CIO hat. Okay, what's the fine print. What are the minimum bars to get in? What's the minimum commitment I need to make? What are some of those nuances? >> Yeah. So, both the Storage as a service, which will be our first offer of many in our portfolio. And the cloud console, which will give you that single web interface to kind of manage, report and kind of thrive in this as a service experience. All that will be released in the first half of the next year. So, we're still frankly defining what that will look like. But we want to make sure that we deliver a solution that can span all segments. From small business to medium business, to the biggest enterprises out there. Globally goal expansion through our channel partners. We're going to have Geos and channel partners fully integrated as well. Service providers as well. As a fundamental important piece of our delivery model and delivering this experience to our customers. So, the fine print Dave will be out early next year. As we GA these releases and bring into market. But ultimate flexibility and choice, up and down the stack and geographically wide is the goal and the intent we plan to deliver that. >> Can you add any color to the sort of product journey, if you will? I even hesitate Sam, to use the word product. Because you're really sort of transferring your mindset into a platform mindset and a services mindset. As opposed to bolting services on top of a price. You sell a product and say okay, service guys you take it from here. You have to sort of rethink, how you deliver. And so you're saying, you start with storage. And then so what can we expect over the next midterm-longterm? >> Yeah. I'll give you an example. Look, we sell a ton of as a service and flexible consumption today. We've been at it for 10 years. In fact in Q2, we sold our annual recurring revenue rate is 1.3 billion growing at 30% very, very pleased. So, this is not new to us. But how you described it Dave is right. We adopt products, customers then pick their product. They pick their service that they want to bolt on. Then they pick their financial payment model they bolted on. So, it's a very good, customized way to build it. That's great. And customers are going to continue to want that and will continue to deliver that. But there is an emerging segment that wants more just kind of think of it as the big easy button. They want to focus on an outcome. Storage as a service is a great example where they're less concerned about what individual product element is part of that. They want it fully managed by Dell Technologies or one of our partners. They don't want to manage it themselves. And of course they want it to be pay-for-use on an OPEX plan that works for their business and gives them that flexibility. So, when customers going forward want to go down this as a service outcome driven path. They're simply going to say, hey, what data service do I want? I want file or block unified object. They pick their data service based on their workload. They pick their performance and capacity tier. There is a term limit, right now we're planning one to five years. Depending on the amount of terms you want to do. And then that's it. It's managed by Dell Technologies. It's on our books from Dell Technologies and it's of course leveraging our great technology portfolio to bring that service and that experience to our customers. So, the service is the product now. It really is making that shift. We are moving into a services driven, services outcome driven set of portfolio and solutions for our customers. >> So, you actually have a lot of data on this. I mean, you talk about a billion dollar business. Maybe talk a little bit about customer uptake. I don't know what you can share in terms of numbers and a number of subscription customers. But I'm really interested in the learnings and the feedback and how that's informed your strategy? >> Yeah. I mean, you're right. Again, we've been at this for many, many years. We have over 2000 customers today that have chosen to take advantage of our flexible consumption and as a service offers that we have today. Nevermind kind of as we move into these kind of turn-key, easy button as a service offers that are to come that early next year. So, we've leveraged all of that learnings and we've heard all of that feedback. It's why it's really important that choice and flexibility is fundamental to the Project Apex strategy. There are some of those customers that they want to build their own. They want to make sure they're running the latest PowerMax or the latest PowerStore. They want to choose their network. They want to choose how they protect it. They want to choose what type of service. They want to cover some of the services. They may want very little from us or vice versa. And then they want to maybe leverage additional, more traditional means to acquire that based on their business goals. That feedback has been loud and clear. But there is that segment that is like, no, no, no. I need to focus more on my business and not my infrastructure. And that's where you're going to see these more turn-key as a service solutions fit that need. Where they want to just define SLAs, outcomes. They want us to take on the burden of managing it for them. So, they can really focus on their applications and their business, not their infrastructure. So, things like metering. Tons of feedback on how we'll want to meter this. Tons of feedback on the types of configurations and scale they're looking for. The applications and workloads that they're targeting for this world. Is very different than the more traditional world. So, we're leveraging all of that information to make sure we deliver our Infrastructure as a service and then eventually Solutions as a service. You think about SAP as a service, VDI as a service. AI machine learning as a service. We'll be moving up the stack as well to meet more of a application integrated as a service experience as well. >> So, I want to ask you. You've given us a couple of data points there, billion dollar plus business. A couple thousand customers. You've got decent average contract values if I do my math right. So, it's not just the little guys. I'm sorry, it's not just the big guys, but there's some fat middle as well that are taking this up. Is that fair to say? >> Totally. I mean, I would say frankly in the enterprise space. It's the mid to larger sides historically and we expect they'll continue to want to kind of choose their best of breed apart. Best of breed of products, Best of breed services. Best of breed financial consumption. Great. And we're in great shape there. We're very confident or competitive and competing in that space today. I think going into the turn-key as a service space that will play up-market. But it will really play down-market, mid-market, smaller businesses. It gives us the opportunity to really drive a solution there. Where they don't have the resources to maybe manage a large storage infrastructure or a backup infrastructure or compute infrastructure. They're going to frankly look to us to provide that experience for them. I think our as a service offers will really play stronger in that mid and kind of lower end of the market. >> So, tell us again. The sort of availability of like the console, for example. When can I actually get-- >> Yeah. >> I can do as a service today. I can buy subscriptions from you. >> Absolutely. >> This is where it all comes together. What's the availability and rollout details? >> Sure. As we look to move to our integrated kind of turn-key as a service offers. The console we're announcing at Dell Technologies World as it's in public preview now. So, for organizations, customers that want to start using it. They can start using it now. The Storage as a service offer is going to be available in the first half of next year. So, we're rapidly kind of working on that now. Looking to early next year to bring that to market. So, you'll see the console and the first as a service offer with storage as a service available in the first half of next year. Readily available to any and everyone that wants to deploy it. We're not that far off right now. But we felt it was really, really important to make sure our customers. Our partners and the industry really understands how important this transformation to as a service and cloud is for Dell Technologies. That's why frankly, externally and internally Project Apex will be that north star to bring our end to end value together across the business. Across our customers, across our teams. And that's why we're really making sure that everybody understands Project Apex and as a services is the future for Dell. And we're very much focused on that. >> As the head of product marketing. This is really a mindset, a cultural change really. You're really becoming the head of service marketing in a way. How are you guys thinking about that mindset shift? >> Well really, it's how am I thinking about it? How is the broader marketing organization thinking about it? How is engineering clearly thinking about it? How is finance thinking about it? How is sale? Like this is transformative across every single function within Dell technologies has a role to play, to do things very differently. Now it's going to take time. It's not going to happen overnight. Various estimates have this as a fairly small percentage of business today in our segments. But we do expect that to start to, and it has started to accelerate ramp. We're preparing for a large percentage of our business to be consumed this way very, very soon. That requires changes in how we sell. Changes in how we market clearly. Changes in how we build products and so forth. And then ultimately, how we account for this has to change. So, we're approaching it I think the right way Dave. Where we're looking at this truly end to end. This isn't a tweak in how we do things or an evolution. This is a revolution. For us to kind of move faster to this model. Again, building on the learnings that we have today with our strong customer base and experience we've built up over the years. But this is a big shift. This isn't an incremental turn of the crank. We know that. I think you expect that. Our customers expect that. And that's the mission we're on with Project Apex. >> Well, I mean, with 30% growth. I mean, that's a clear indicator and people like growth. No doubt. That's a clear indicator that customers are glomming onto this. I think many folks want to buy this way, and I think increasingly that's how they buy SaaS. That's how they buy cloud. Why not buy infrastructure the same way? Give us your closing thoughts Sam. What are the big takeaways? >> Yeah. The big takeaways is from a Dell Technologies perspective. Project Apex is that strategic vision of bringing together our as a service and cloud capabilities into a easy to consume, simple, flexible offer. That provides ultimate choice to our customers. Look, the market has spoken. We're going to be living in a hybrid multicloud world. I think the market is also starting to speak. That they want that to be an as a service experience, regardless if it's on or off ground. It's our job. It's our responsibility to bring that ease. That simplicity and elegance to the on-prem world. It's not certainly not going anywhere. So, that's the mission that we're on with Project Apex. I like the hand we've been dealt. I like the infrastructure and the solutions that we have across our portfolio. And we're going to be after this, for the next couple of years. To refine this and build this out for our customers. This is just the beginning. >> Wow, it's awesome. Thank you so much for coming to theCUBE. We're seeing the cloud model. It's extending on-prem, cloud, multicloud it's going to the Edge. And the way in which customers want to transact business is moving at the same direction. So, Sam good luck with this and thanks so much. Appreciate your time. >> Yeah, thanks Dave. Thanks everyone. Take care. >> All right and thank you for watching. This is Dave Vellante for theCUBE and our continuing coverage of Dell Tech World 2020. The virtual CUBE. We'll be right back right after this short break. (gentle music)

hi everyone this is dave vellante of the cube and i want to share with you an exciting development with some financial support from hpe the cube is hosting exascale day on friday october 16th high performance technical and business communities are coming together to celebrate exascale day now exascale day is happening on october 18th that's 10 18 as in 10 to the power of 18. now on that day we celebrate the scientists and researchers who make breakthrough discoveries with the assistance of some of the largest supercomputers in the world 10 to the power of 18 is a 1 with 18 zeros after that's six commas or seis comas for you russ hannemann fans of silicon valley fame remember he could only get to tres comas and he became suicidal when his net worth dropped below a billion aka dos comas now an exit scale computer exascale supercomputer can do math at the rate of 10 to the power of 18 calculations per second those are those calculations are called flops or floating point operations per second that's a billion calculations per second or exa-flops now we haven't hit that level yet that exit scale level but dollars to donuts we'll buy we will by next year now today we can do header scale computing that's 10 to the power of 15 calculations per second and we entered the petascale era in 2007 before that was the terrascale era it's kind of like dinosaurs which began in the middle of the dot-com boom in 1997. that's 10 to the 12th calculations per second or trillion per second so we can almost get our heads around that and all the way back in 1972 we had the first gigascale computer which was one times ten to the ninth yeah that's more russ hannemann's speed sorry rush you're not invited to at the exascale day party but you are so go to events dot cube365.net slash 10-18 exascale day it's right there in the screen so check it out mark your calendar we'll be sending out notices so don't worry if you're driving right now we have some of the smartest people in the world joining us they're going to share how innovations with supercomputing are changing the world in healthcare space exploration artificial intelligence and these other mind-melting projects we're super excited to be participating in this program we look forward to some great conversations october 16th right before exascale day put on your calendar see you there

>> Announcer: theCube presents HPE Discover 2022, brought to you by HPE. >> Hey, everyone. Welcome back to Las Vegas. This is Lisa Martin with Dave Vellante live at HPE Discover '22. Dave, it's great to be here. This is the first Discover in three years and we're here with about 7,000 of our closest friends. >> Yeah. You know, I tweeted out this, I think I've been to 14 Discovers between the U.S. and Europe, and I've never seen a Discover with so much energy. People are not only psyched to get back together, that's for sure, but I think HPE's got a little spring in its step and it's feeling more confident than maybe some of the past Discovers that I've been to. >> I think so, too. I think there's definitely a spring in the step and we're going to be unpacking some of that spring next with one of our alumni who joins us, Keith White's here, the executive vice president and general manager of GreenLake Cloud Services. Welcome back. >> Great. You all thanks for having me. It's fantastic that you're here and you're right, the energy is crazy at this show. It's been a lot of pent up demand, but I think what you heard from Antonio today is our strategy's changing dramatically and it's really embracing our customers and our partners. So it's great. >> Embracing the customers and the partners, the ecosystem expansion is so critical, especially the last couple of years with the acceleration of digital transformation. So much challenge in every industry, but lots of momentum on the GreenLake side, I was looking at the Q2 numbers, triple digit growth in orders, 65,000 customers over 70 services, eight new services announced just this morning. Talk to us about the momentum of GreenLake. >> The momentum's been fantastic. I mean, I'll tell you, the fact that customers are really now reaccelerating their digital transformation, you probably heard a lot, but there was a delay as we went through the pandemic. So now it's reaccelerating, but everyone's going to a hybrid, multi-cloud environment. Data is the new currency. And obviously, everyone's trying to push out to the Edge and GreenLake is that edge to cloud platform. So we're just seeing tons of momentum, not just from the customers, but partners, we've enabled the platform so partners can plug into it and offer their solutions to our customers as well. So it's exciting and it's been fun to see the momentum from an order standpoint, but one of the big numbers that you may not be aware of is we have over a 96% retention rate. So once a customer's on GreenLake, they stay on it because they're seeing the value, which has been fantastic. >> The value is absolutely critically important. We saw three great big name customers. The Home Depot was on stage this morning, Oak Ridge National Laboratory was as well, Evil Geniuses. So the momentum in the enterprise is clearly present. >> Yeah. It is. And we're hearing it from a lot of customers. And I think you guys talk a lot about, hey, there's the cloud, data and Edge, these big mega trends that are happening out there. And you look at a company like Barclays, they're actually reinventing their entire private cloud infrastructure, running over a hundred thousand workloads on HPE GreenLake. Or you look at a company like Zenseact, who's basically they do autonomous driving software. So they're doing massive parallel computing capabilities. They're pulling in hundreds of petabytes of data to then make driving safer and so you're seeing it on the data front. And then on the Edge, you look at anyone like a Patrick Terminal, for example. They run a whole terminal shipyard. They're getting data in from exporters, importers, regulators, the works and they have to real-time, analyze that data and say, where should this thing go? Especially with today's supply chain challenges, they have to be so efficient, that it's just fantastic. >> It was interesting to hear Fidelma, Keith, this morning on stage. It was the first time I'd really seen real clarity on the platform itself and that it's obviously her job is, okay, here's the platform, now, you guys got to go build on top of it. Both inside of HPE, but also externally, so your ecosystem partners. So, you mentioned the financial services companies like Barclays. We see those companies moving into the digital world by offering some of their services in building their own clouds. >> Keith: That's right. >> What's your vision for GreenLake in terms of being that platform, to assist them in doing that and the data component there? >> I think that was one of the most exciting things about not just showcasing the platform, but also the announcement of our private cloud enterprise, Cloud Service. Because in essence, what you're doing is you're creating that framework for what most companies are doing, which is they're becoming cloud service providers for their internal business units. And they're having to do showback type scenarios, chargeback type scenarios, deliver cloud services and solutions inside the organization so that open platform, you're spot on. For our ecosystem, it's fantastic, but for our customers, they get to leverage it as well for their own internal IT work that's happening. >> So you talk about hybrid cloud, you talk about private cloud, what's your vision? You know, we use this term Supercloud. This in a layer that goes across clouds. What's your thought about that? Because you have an advantage at the Edge with Aruba. Everybody talks about the Edge, but they talk about it more in the context of near Edge. >> That's right. >> We talked to Verizon and they're going far Edge, you guys are participating in that, as well as some of your partners in Red Hat and others. What's your vision for that? What I call Supercloud, is that part of the strategy? Is that more longer term or you think that's pipe dream by Dave? >> No, I think it's really thoughtful, Dave, 'cause it has to be part of the strategy. What I hear, so for example, Ford's a great example. They run Azure, AWS, and then they made a big deal with Google cloud for their internal cars and they run HPE GreenLake. So they're saying, hey, we got four clouds. How do we sort of disaggregate the usage of that? And Chris Lund, who is the VP of information technology at Liberty Mutual Insurance, he talked about it today, where he said, hey, I can deliver these services to my business unit. And they don't know, am I running on the public cloud? Am I running on our HPE GreenLake cloud? Like it doesn't matter to the end user, we've simplified that so much. So I think your Supercloud idea is super thoughtful, not to use the super term too much, that I'm super excited about because it's really clear of what our customers are trying to accomplish, which it's not about the cloud, it's about the solution and the business outcome that gets to work. >> Well, and I think it is different. I mean, it's not like the last 10 years where it was like, hey, I got my stuff to work on the different clouds and I'm replicating as much as I can, the cloud experience on-prem. I think you guys are there now and then to us, the next layer is that ecosystem enablement. So how do you see the ecosystem evolving and what role does Green Lake play there? >> Yeah. This has been really exciting. We had Tarkan Maner who runs Nutanix and Karl Strohmeyer from Equinix on stage with us as well. And what's happening with the ecosystem is, I used to say, one plus one has to equal three for our customers. So when you bring these together, it has to be that scenario, but we are joking that one plus one plus one equals five now because everything has a partner component to it. It's not about the platform, it's not about the specific cloud service, it's actually about the solution that gets delivered. And that's done with an ISV, it's done with a Colo, it's done even with the Hyperscalers. We have Azure Stack HCI as a fully integrated solution. It happens with managed service providers, delivering managed services out to their folks as well. So that platform being fully partner enabled and that ecosystem being able to take advantage of that, and so we have to jointly go to market to our customers for their business needs, their business outcomes. >> Some of the expansion of the ecosystem. we just had Red Hat on in the last hour talking about- >> We're so excited to partner with them. >> Right, what's going on there with OpenShift and Ansible and Rel, but talk about the customer influence in terms of the expansion of the ecosystem. We know we've got to meet customers where they are, they're driving it, but we know that HPE has a big presence in the enterprise and some pretty big customer names. How are they from a demand perspective? >> Well, this is where I think the uniqueness of GreenLake has really changed HPE's approach with our customers. Like in all fairness, we used to be a vendor that provided hardware components for, and we talked a lot about hardware costs and blah, blah, blah. Now, we're actually a partner with those customers. What's the business outcome you're requiring? What's the SLA that we offer you for what you're trying to accomplish? And to do that, we have to have it done with partners. And so even on the storage front, Qumulo or Cohesity. On the backup and recovery disaster recovery, yes, we have our own products, but we also partner with great companies like Veeam because it's customer choice, it's an open platform. And the Red Hat announcement is just fantastic. Because, hey, from a container platform standpoint, OpenShift provides 5,000 plus customers, 90% of the fortune 500 that they engage with, with that opportunity to take GreenLake with OpenShift and implement that container capabilities on-prem. So it's fantastic. >> We were talking after the keynote, Keith Townsend came on, myself and Lisa. And he was like, okay, what about startups? 'Cause that's kind of a hallmark of cloud. And we felt like, okay, startups are not the ideal customer profile necessarily for HPE. Although we saw Evil Geniuses up on stage, but I threw out and I'd love to get your thoughts on this that within companies, incumbents, you have entrepreneurs, they're trying to build their own clouds or Superclouds as I use the term, is that really the target for the developer audience? We've talked a lot about OpenShift with their other platforms, who says as a partner- >> We just announced another extension with Rancher and- >> Yeah. I saw that. And you have to have optionality for developers. Is that the way we should think about the target audience from a developer standpoint? >> I think it will be as we go forward. And so what Fidelma presented on stage was the new developer platform, because we have come to realize, we have to engage with the developers. They're the ones building the apps. They're the ones that are delivering the solutions for the most part. So yeah, I think at the enterprise space, we have a really strong capability. I think when you get into the sort of mid-market SMB standpoint, what we're doing is we're going directly to the managed service and cloud service providers and directly to our Disty and VARS to have them build solutions on top of GreenLake, powered by GreenLake, to then deliver to their customers because that's what the customer wants. I think on the developer side of the house, we have to speak their language, we have to provide their capabilities because they're going to start articulating apps that are going to use both the public cloud and our on-prem capabilities with GreenLake. And so that's got to work very well. And so you've heard us talk about API based and all of that sort of scenario. So it's an exciting time for us, again, moving HPE strategy into something very different than where we were before. >> Well, Keith, that speaks to ecosystem. So I don't know if you were at Microsoft, when the sweaty Steve Ballmer was working with the developers, developers. That's about ecosystem, ecosystem, ecosystem. I don't expect we're going to see Antonio replicating that. But that really is the sort of what you just described is the ecosystem developing on top of GreenLake. That's critical. >> Yeah. And this is one of the things I learned. So, being at Microsoft for as long as I was and leading the Azure business from a commercial standpoint, it was all about the partner and I mean, in all fairness, almost every solution that gets delivered has some sort of partner component to it. Might be an ISV app, might be a managed service, might be in a Colo, might be with our hybrid cloud, with our Hyperscalers, but everything has a partner component to it. And so one of the things I learned with Azure is, you have to sell through and with your ecosystem and go to that customer with a joint solution. And that's where it becomes so impactful and so powerful for what our customers are trying to accomplish. >> When we think about the data gravity and the value of data that put massive potential that it has, even Antonio talked about it this morning, being data rich but insights poor for a long time. >> Yeah. >> Every company in today's day and age has to be a data company to be competitive, there's no more option for that. How does GreenLake empower companies? GreenLake and its ecosystem empower companies to really live being data companies so that they can meet their customers where they are. >> I think it's a really great point because like we said, data's the new currency. Data's the new gold that's out there and people have to get their arms around their data estate. So then they can make these business decisions, these business insights and garner that. And Dave, you mentioned earlier, the Edge is bringing a ton of new data in, and my Zenseact example is a good one. But with GreenLake, you now have a platform that can do data and data management and really sort of establish and secure the data for you. There's no data latency, there's no data egress charges. And which is what we typically run into with the public cloud. But we also support a wide range of databases, open source, as well as the commercial ones, the sequels and those types of scenarios. But what really comes to life is when you have to do analytics on that and you're doing AI and machine learning. And this is one of the benefits I think that people don't realize with HPE is, the investments we've made with Cray, for example, we have and you saw on stage today, the largest supercomputer in the world. That depth that we have as a company, that then comes down into AI and analytics for what we can do with high performance compute, data simulations, data modeling, analytics, like that is something that we, as a company, have really deep, deep capabilities on. So it's exciting to see what we can bring to customers all for that spectrum of data. >> I was excited to see Frontier, they actually achieve, we hosted an event, co-produced event with HPE during the pandemic, Exascale day. >> Yeah. >> But we weren't quite at Exascale, we were like right on the cusp. So to see it actually break through was awesome. So HPC is clearly a differentiator for Hewlett Packard Enterprise. And you talk about the egress. What are some of the other differentiators? Why should people choose GreenLake? >> Well, I think the biggest thing is, that it's truly is a edge to cloud platform. And so you talk about Aruba and our capabilities with a network attached and network as a service capabilities, like that's fairly unique. You don't see that with the other companies. You mentioned earlier to me that compute capabilities that we've had as a company and the storage capabilities. But what's interesting now is that we're sort of taking all of that expertise and we're actually starting to deliver these cloud services that you saw on stage, private cloud, AI and machine learning, high performance computing, VDI, SAP. And now we're actually getting into these industry solutions. So we talked last year about electronic medical records, this year, we've talked about 5g. Now, we're talking about customer loyalty applications. So we're really trying to move from these sort of baseline capabilities and yes, containers and VMs and bare metal, all that stuff is important, but what's really important is the services that you run on top of that, 'cause that's the outcomes that our customers are looking at. >> Should we expect you to be accelerating? I mean, look at what you did with Azure. You look at what AWS does in terms of the feature acceleration. Should we expect HPE to replicate? Maybe not to that scale, but in a similar cadence, we're starting to see that. Should we expect that actually to go faster? >> I think you couched it really well because it's not as much about the quantity, but the quality and the uses. And so what we've been trying to do is say, hey, what is our swim lane? What is our sweet spot? Where do we have a superpower? And where are the areas that we have that superpower and how can we bring those solutions to our customers? 'Cause I think, sometimes, you get over your skis a bit, trying to do too much, or people get caught up in the big numbers, versus the, hey, what's the real meat behind it. What's the tangible outcome that we can deliver to customers? And we see just a massive TAM. I want to say my last analysis was around $42 billion in the next three years, TAM and the Azure service on-prem space. And so we think that there's nothing but upside with the core set of workloads, the core set of solutions and the cloud services that we bring. So yeah, we'll continue to innovate, absolutely, amen, but we're not in a, hey we got to get to 250 this and 300 that, we want to keep it as focused as we can. >> Well, the vast majority of the revenue in the public cloud is still compute. I mean, not withstanding, Microsoft obviously does a lot in SaaS, but I'm talking about the infrastructure and service. Still, well, I would say over 50%. And so there's a lot of the services that don't make any revenue and there's that long tail, if I hear your strategy, you're not necessarily going after that. You're focusing on the quality of those high value services and let the ecosystem sort of bring in the rest. >> This is where I think the, I mean, I love that you guys are asking me about the ecosystem because this is where their sweet spot is. They're the experts on hyper-converged or databases, a service or VDI, or even with SAP, like they're the experts on that piece of it. So we're enabling that together to our customers. And so I don't want to give you the impression that we're not going to innovate. Amen. We absolutely are, but we want to keep it within that, that again, our swim lane, where we can really add true value based on our expertise and our capabilities so that we can confidently go to customers and say, hey, this is a solution that's going to deliver this business value or this capability for you. >> The partners might be more comfortable with that than, we only have one eye sleep with one eye open in the public cloud, like, okay, what are they going to, which value of mine are they grab next? >> You're spot on. And again, this is where I think, the power of what an Edge to cloud platform like HPE GreenLake can do for our customers, because it is that sort of, I mentioned it, one plus one equals three kind of scenario for our customers so. >> So we can leave your customers, last question, Keith. I know we're only on day one of the main summit, the partner growth summit was yesterday. What's the feedback been from the customers and the ecosystem in terms of validating the direction that HPE is going? >> Well, I think the fantastic thing has been to hear from our customers. So I mentioned in my keynote recently, we had Liberty Mutual and we had Texas Children's Hospital, and they're implementing HPE GreenLake in a variety of different ways, from a private cloud standpoint to a data center consolidation. They're seeing sustainability goals happen on top of that. They're seeing us take on management for them so they can take their limited resources and go focus them on innovation and value added scenarios. So the flexibility and cost that we're providing, and it's just fantastic to hear this come to life in a real customer scenario because what Texas Children is trying to do is improve patient care for women and children like who can argue with that. >> Nobody. >> So, yeah. It's great. >> Awesome. Keith, thank you so much for joining Dave and me on the program, talking about all of the momentum with HPE Greenlake. >> Always. >> You can't walk in here without feeling the momentum. We appreciate your insights and your time. >> Always. Thank you you for the time. Yeah. Great to see you as well. >> Likewise. >> Thanks. >> For Keith White and Dave Vellante, I'm Lisa Martin. You're watching theCube live, day one coverage from the show floor at HPE Discover '22. We'll be right back with our next guest. (gentle music)

>> Announcer: From around the globe it's theCUBE with digital coverage of HPE GreenLake Day made possible by Hewlett Packard Enterprise. >> Welcome everybody to this spotlight session here at GreenLake Day and we're going to dig into high-performance computing. Let me first bring in Pete Ungaro who's the GM for HPC and Mission Critical Solutions at Hewlett Packard Enterprise. And then we're going to pivot to Addison Snell, who's the CEO of research firm Intersect360. So Pete started with you welcome and really a pleasure to have you here. I want to first start off by asking you what are the key trends that you see in the HPC and super computing space. And I really appreciate if you could talk about how customer consumption patterns are changing. >> Yeah, appreciate that Dave and thanks for having me. I think the biggest thing that we're seeing is just the massive growth of data. And as we get larger and larger data sets larger and larger models happen and we're having more and more new ways to compute on that data. So new algorithms like AI would be a great example of that. And as people are starting to see this, especially as they're going through digital transformations, more and more people I believe can take advantage of HPC but maybe don't know how and don't know how to get started. And so they're looking for how to get going into this environment. And many customers that are long-time HPC customers just consume it on their own data centers, they have that capability but many don't. And so they're looking at how can I do this? Do I need to build up that capability myself? Do I go to the Cloud? What about my data and where that resides? So there's a lot of things that are going into thinking through how do I start to take advantage of this new infrastructure? >> Excellent, I mean, we all know HPC workloads. You're talking about fording research and discovery for some of the toughest and most complex problems particularly those that are affecting society. So I'm interested in your thoughts on how you see GreenLake helping in these endeavors specifically. >> Yeah, one of the most exciting things about HPC is just the impact that it has. Everywhere from building safer cars and airplanes to looking at climate change to finding new vaccines for things like COVID that we're all dealing with right now. So one of the biggest things is how do we take advantage of that and use that to benefit society overall. And as we think about implementing HPC, how do we get started and then how do we grow and scale as we get more and more capabilities. So that's the biggest things that we're seeing on that front. >> Yeah, okay, so just about a year ago you guys launched the GreenLake initiative and the whole complete focus on as a service. So I'm curious as to how the new GreenLake services the HPC services specifically as it relates to GreenLake, how do they fit into HP's overall high-performance computing portfolio and the strategy? >> Yeah, great question. GreenLake is a new consumption model for us. So it's a very exciting. We keep our entire HPC portfolio that we have today but extend it with GreenLake and offer customers expanded consumption choices. So customers that potentially are dealing with the growth of their data or they're moving to digital transformation applications, they can use GreenLake just easily scale up from workstations to manage their system costs or operational costs or if they don't have staff to expand their environment, GreenLake provides all of that in a managed infrastructure for them. So if they're going from like a pilot environment, I've been to a production environment over time, GreenLake enables them to do that very simply and easily without having to have all that internal infrastructure people, computer data centers, et cetera, GreenLake provides all that for them. So they can have a turnkey solution for HPC. >> So a lot easier entry strategy is a key word that you use there was choice though. So basically you're providing optionality, you're not necessarily forcing them into a particular model, is that correct? >> Yeah, 100% Dave. What we want to do is just expand the choices so customers can buy and acquire and use that technology to their advantages. Whether they're large or small, whether they're a startup or a fortune 500 company, whether they have their own data centers or they want to use a colo facility, whether they have their own staff or not. We want to just provide them the opportunity to take advantage of this leading edge resource. >> Very interesting, Pete, I really appreciate the perspectives that you guys are bringing to the market. I mean, it seems to me it's going to really accelerate broader adoption of high-performance computing to the masses, really giving them an easier entry point. I want to bring in now Addison Snell to the discussion. Addison, he's a CEO, as I said of Intersect360 which in my view is the world's leading market research company focused on HPC. Addison you've been following this space for a while. You're an expert, you've seen a lot of changes over the years. What do you see as the critical aspects in the market specifically as it relates toward this as a service delivery that we were just discussing with Pete? And I wonder if you could sort of work in there the benefits in terms of in your view how it's going to affect HPC usage broadly. >> Yeah, good morning Dave, and thanks very much for having me. Pete it's great to see you again. So we've been tracking a lot of these utility computing models in high-performance computing for years. Particularly as most of the usage by revenue is actually by commercial endeavors using high-performance computing for their R and D and engineering projects and the like. And cloud computing has been a major portion of that and has the highest growth rate in the market right now where we're seeing this double digit growth that accounted for about $1.4 billion of the high-performance computing industry last year. But the bigger trend and which makes GreenLake really interesting is that we saw an additional about a billion dollars worth of spending outside what was directly measured in the cloud portion of the market in areas that we deemed to be cloud-like which were as a service types of contracts that were still utility computing, but they might be under a software as a service portion of a budget under software or some other managed services type of contract that the user wasn't reporting directly as cloud but was certainly influenced by utility computing. And I think that's going to be a really dominant portion of the market going forward when we look at a growth rate and where the market's been evolving. >> So that's interesting. I mean, basically you're saying this utility model is not brand new, we've seen that for years. Cloud was obviously a catalyst that gave that a boost. What is new you're saying is, and I'll say it this way. I'd love to get your independent perspective on this is sort of the definition of cloud is expanding where we people always say, it's not a place, it's an experience and I couldn't agree more. But I wonder if you could give us your independent perspective on that, both on the thoughts of what I just said but also how would you rate HPE position in this market? >> Well, you're right absolutely that the definition of cloud is expanding. And that's a challenge when we run our surveys that we try to be pedantic in a sense and define exactly what we're talking about. And that's how we're able to measure both the direct usage of a typical public cloud but also a more flexible notion of as a service. Now you asked about HPE in particular and that's extremely relevant, not only with GreenLake, but with their broader presence in high-performance computing. HPE is the number one provider of systems for high-performance computing worldwide. And that's largely based on the breadth of HPE's offerings in addition to their performance at various segments. So picking up a lot of the commercial market with our HPE Apollo Gen10 plus, they hit a lot of big memory configurations with the Superdome Flex and scale up to some of the most powerful supercomputers in the world with the HPE Cray EX platforms that go into some of the leading national labs. Now GreenLake gives them an opportunity to offer this kind of flexibility to customers rather than committing all at once to a particular purchase price. But if you want to do position those on a utility computing basis, pay for them as a service without committing to a particular public cloud, I think that's an interesting role for GreenLake to play in the market. >> Yeah, yeah it's interesting. I mean, earlier this year we celebrated Exascale Day with the support from HPE and it really is all about a community and an ecosystem. Is a lot of comradery going on in the space that you guys are deep into. Addison, it says we can wrap what should observe as expect in this HPC market, in this space over the next few years? >> Yeah, that's a great question what to expect because if 2020 has taught us anything it's the hazards of forecasting where we think the market is going. Like when we put out a market forecast, we tend not to look at huge things like unexpected pandemics or wars but it's relevant to the topic here. Because as I said, we were already forecasting cloud and as a service models growing. Anytime you get into uncertainty where it becomes less easy to plan for where you want to be in two years, three years, five years, that model speaks well to things that are cloud or as a service to do very well flexibly. And therefore, when we look at the market and plan out where we think it is in 2020, 2021, anything that accelerates uncertainty actually is going to increase the need for something like GreenLake or an as a service or cloud type of environment. So we're expecting those sorts of deployments to come in over and above where we were already previously expected them in 2020, 2021. Because as a service deals well with uncertainty and that's just the world we've been in recently. >> I think those are great comments and a really good framework. And we've seen this with the pandemic, the pace at which the technology industry in particular and of course HPE specifically have responded to support that. Your point about agility and flexibility being crucial. And I'll go back to something earlier that Pete said around the data, the sooner we can get to the data to analyze things, whether it's compressing the time to a vaccine or pivoting our businesses, the better off we are. So I want to thank Pete and Addison for your perspectives today. Really great stuff, guys, thank you. >> Yeah, thank you. >> Thank you. >> All right, keep it right there for more great insights and content. You're watching GreenLake Day. (ambient music)