 From around the globe, it's theCUBE with digital coverage of Exascale Day, made possible by Hewlett Packard Enterprise. Welcome everyone to theCUBE 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, sir. Hey, well, let's evangelize Exascale a little bit. You know, what's exciting you in regards to the coming of Exascale computing? Well, there's a couple of things really. For me, historically, I've worked in supercomputing for many years and I have seen the coming of several milestones from, you know, actually I'm old enough to remember gigaflops coming through and pteroflops and petaflops. Exascale 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 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. And if you look at some of the most powerful computers around today, they've really helped with the pandemic, COVID. 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 do more which ultimately will aid humanity. 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 better 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? Well, you mentioned the pandemic and you know, the president of the United States was tweeting this morning that he was upset that you know, the FDA in the US is not allowing the vaccine to proceed as fast as you'd like. In fact, the FDA is loosening some of its 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 and concerns. Is that work that is going on today or are you saying 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 first a disclaimer, I am not a geneticist. I am not a biochemist. My son is, he tries to explain it to me and it tends to go in one ear and out the other. I just merely build the machines he uses. So we're sort of even on that front. 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 COVID-19 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 smearing a few people and hoping it worked. I think we're slightly beyond that. The FDA has rules and regulations for a reason. We, you don't have to go back far in our history to understand the nature of drugs that work for 99% of the population, you know? But I think Exascale, widely available Exascale 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 1% 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. Personally, are we at a point where we still need human trials? Yes, do we still need due diligence? Yes, 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 chance that we're actually going to get it right and maybe compress that time to market. But talk about some of the problems that you're working on and the challenges, for example, with the UK government and maybe others that you can share with us. Help us understand kind of what you're hoping to accomplish. So within the United Kingdom, there was a report published 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 science. And there was a case, a science case done for Exascale. I'm not a scientist. 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-depth, but it will be very detailed, an in-depth conversation with Professor Mark Parsons from the Edinburgh Parallel Computing Centres, the director there and the dean of research at Edinburgh University. And I'm going to be talking to him about the Asimov program 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 exascale 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 Rolls-Royce example that you gave. If I understood it, it wasn't so much safety as it was. You said efficiency, and so that's what? Fuel consumption? It's partly fuel consumption. It is, of course, safety. 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. They fire off a little explosive and they fire a fan belt, a fan blade off to make sure that it doesn't go through the cowling. The reason they do that is there has been in the past 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 won 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. 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, 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. We still need to build an engine which costs in excess of $10 million and blow the fan blade off it. So you're talking about some pretty complex simulations, obviously. What are some of the barriers and the breakthroughs that are kind of required to do some of these things that you're talking about that Exascale is going to enable? Presumably they're obviously technical barriers, but maybe you could shed some light on that. Some of them are very prosaic. So for example, power. Exascale machines consume a lot of power. 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. An Exascale computer is going to generate a lot more heat. Several megawatts, actually. 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. 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 gigaflot 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 it's going to have to evolve. 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. Consequently, one of the things that we're doing is that we have a lot of centres of excellence. We will provide, well, I hate to say the word, provide. We sell supercomputers and once the machine has gone in, we work very closely with the establishments, create centres of excellence to get the best out of the machines, to improve the software. 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. 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, a fastest supercomputer on the planet, but if you can't afford to use it, what good is it? You mentioned power. And then the flip side of that coin is, of course, cooling. Reduce the power consumption. But how challenging is it to cool these systems? It's an engineering problem. We have, you know, data centres in Iceland where it gets, you know, it doesn't get too warm. We have a big air-cooled data centre 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. Right. Other ways to do it, you know, is to take the heat and use it commercially. There's a lovely story of they take the hot water out of the supercomputer in the Nordics and then they pump it into a brewery to keep the mash tons warm. You know, 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 probe there? What are you hoping to learn? So I think some of the things that are going to be interesting to uncover is just the breadth of science that can take advantage of exascale. You know, there are many things going on that people hear about. You know, people are interested in, you know, the Nobel Prize. You might have no idea what it means, but the Nobel Prize for Physics was awarded to do with research into black holes. You know, fascinating and truly insightful physics. Could it benefit from exascale? I have no idea. I really don't. You know, one of the most profound pieces of knowledge in the last few hundred years has been the theory of relativity. You know, an Austrian patent clerk wrote E equals MC squared on the back of an envelope. And voila! I don't believe any form of exascale computing would have helped him get there any faster. Right. That may be flippant, but I think the point is that there are areas in terms of weather prediction, climate prediction, drug discovery, material knowledge, engineering 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. And that's the purpose of computing. It's not the data that comes out and it's the insight we get from it. Yeah, I often say data is plentiful. Insights are not. Ben, you're a bit of an industry historian, so I've got to ask you. You mentioned gigaflops before, which I think goes back to the early 1970s. Actually, the 80s. Well, the history of supercomputing goes back even before that. I thought Seymour Cray was the father of supercomputing, but perhaps you have another point of view as to the origination of high-performance computing slash supercomputing. Oh, yes. This is one for all my colleagues globally. Arguably, he says, getting ready to be attacked from all sides. Arguably, you know, computing, 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 have done, 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 British Science Museum archives to look at how computing has evolved from things like the Pascaline from Blaise Pascal, the Napier's bones, the Babbage's machines to look all the way through the analog machines, what Conrad Zeus was doing on a desktop. 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 human race to look at solutions and be these kick-started by 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 it's a great conversation. You're a brilliant guest. I love this talk. And of course, as the saying goes, success has many fathers. So there's probably a few Polish mathematicians in the original Enigma project as well. I think they drove the algorithm. I think the problem is that the work of Tommy Flowers is the person who took the algorithms and the work 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? People always remember Turing. Very few people remember Tommy Flowers, who actually had to turn the great work into a working machine. Yeah, super computer. Team Sport. Well, Ben, it was 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 thanks so much for coming on The Cube. 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.