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]