[Music] [Applause] I'll Buena Burris and welcome to another cube conversation from our fantastic studios in beautiful palo alto california today we're going to be talking about what infrastructure can do to accelerate AI and specifically we're gonna use a relationship a burgeoning relationship between PDN and nvidia to describe what we can do to accelerate AI workloads by using higher performance smarter and more focused of infrastructure for computing now to have this conversation we've got two great guests here we've got Kurt ku kind who is the senior director of marketing at ddn and also Darren Johnson is a global director of technical marketing for enterprise and NVIDIA Kurt Gerron welcome to the cube thanks for thank you very much so let's get going on this because this is a very very important topic and I think it all starts with this notion of that there is a relationship that you guys have put forward Kurt once you describe it sure well so what we're announcing today is ddn's a3i architecture powered by Nvidia so it is a full rack level solution a reference architecture that's been fully integrated and fully tested to deliver an AI infrastructure very simply very completely so if we think about how this is gonna or why this is important AI workloads clearly have a special stress on underlying technology Darin talk to us a little bit about the nature of these workloads and why in particular things like GPUs and other technologies are so important to make them go fast absolutely and as you probably know AI is all about the data whether you're doing medical imaging whether you're doing natural language processing whatever it is it's all driven by the data the more data that you have the better results that you get but to drive that data into the GPUs you need great IO and that's why we're here today to talk about ddn and the partnership of how to bring that I owe to the GPUs on our dgx platforms so if we think about what you described a lot of small files off and randomly just riveted with nonetheless very high-profile jobs that just can't stop midstream and start over absolutely and if you think about the history of high-performance computing which is very similar to a I really I owe is just that lots of files you have to get it they're low latency high throughput and that's why ddn's probably nearly twenty years of experience working in that exact same domain is perfect because you get the parallel file system which gives you that throughput gives you that low latency just helps drive the GPU so we you'd mention HPC from 20 years of experience now it used to be that HPC you'd have scientists with a bunch of graduate students setting up some of these big honkin machines but now we're moving into the commercial domain you don't have graduate students running around you don't have very low cost high quality people you're you know a lot of administrators who nonetheless good people but a lot to learn so how does this relationship actually start making or bringing AI within reach of the commercial world exactly where this reference architecture comes in right so a customer doesn't need to start from scratch they have a design now that allows them to quickly implement AI it's something that's really easily deployable we've fully integrated this solution ddn has made changes to our parallel file system appliance to integrate directly within the DG x1 environment makes that even easier to deploy from there and extract the maximum performance out of this without having to run around and tune a bunch of knobs change a bunch of settings it's really gonna work out of the box and the you know nvidia has done more than just the DG x1 it's more than hardware you've done a lot of optimization of different of AI toolkits if Sarah I'm talking what about that Darin yeah so I mean talking about the example I use researchers in the past with HPC what we have today are data scientists data scientists understand pie tours they understand tensorflow they understand the frameworks they don't want to understand the underlying filesystem networking RDMA InfiniBand any of that they just want to be able to come in run their tensorflow get the data get the results and just turn that keep turning that whether it's a single GPU or 90 Jex's or as many dejection as you want so this solution helps bring that to customers much easier so those data scientists don't have to be system administrators so a reference architecture that makes things easier but that's more than just for some of these commercial things it's also the overall ecosystem new application providers application developers how is this going to impact the aggregate ecosystem it's growing up around the need to do AI related outcomes well I think one point that Darrin was getting to you there and one of the big effects is also as these ecosystems reach a point where they're going to need to scale right there's somewhere where ddn has tons of experience right so many customers are starting off with smaller data sets they still need the performance a parallel file system in that case is going to deliver that performance but then also as they grow right going from one GPU to 90 G X's is going to be an incredible amount of both performance scalability that they're going to need from their i/o as well as probably capacity scalability and that's another thing that we've made easy with a3i is being able to scale that environment seamlessly within a single namespace so that people don't have to deal with a lot of again tuning and turning of knobs to make this stuff work really well and drive those outcomes that they need as they're successful right so in the end it is the application that's most important to both of us right it's it's not the infrastructure it's making the discoveries faster it's processing information out in the field faster it's doing analysis of the MRI faster it's you know helping the doctors helping the anybody who's using this to really make faster decisions better decisions exactly and just to add to that I mean in automotive industry you have datasets that are from 50 to 500 petabytes and you need access to all that data all the time because you're constantly training and Retraining to create better models to create better autonomous vehicles and you need you need the performance to do that ddn helps bring that to bear and with this reference architecture simplifies it so you get the value add of nvidia gpus plus its ecosystem of software plus DD on its match made in heaven Darren Johnson Nvidia Curt Koo Kien ddn thanks very much for being on the cube thank you very much and I'm Peter burrs and once again I'd like to thank you for watching this cube conversation until next time [Music]