 Hey, welcome back everybody. Jeff Frick here at theCUBE. We are on the ground in Austin, Texas on a really special field trip that we're excited to be here. It's the Dell EMC High Performance Computing and Machine Learning Innovation Labs. They've got every type of configuration of hardware, software. This is where they put it together. They test all the configs, pre-build solutions that really design optimum solutions for the customers. And we're excited to have with us our next guest. She's Garima Cocher. She's on the technical staff and the senior principal engineer at Dell EMC. Welcome. Thank you. What a cool place that you work here. That's right. That's right. Flashing lights, tons of drives, every kind of potential hardware configuration that you guys can ever put together. Exactly. I've been on this team 14 years and now you can tell why I'm still here. There's so much to do and so much to learn. So it's a big day. We're talking about really the AI kind of flavor of this lab and the machine learning where before it was really, it's always been a high-performance computing lab. What is, from your perspective, what's kind of changing in the landscape from high-performance computing which has been around for a long time into more of the AI and machine learning and deep learning and stuff we hear about much more in business context today? Right. So you're right. This lab's been around for a while and we've been primarily focused on the high-performance computing piece. And we've added in AI. High-performance computing has applicability across a broad range of industries. So not just national labs and supercomputers but commercial space as well. And our lab, we've done a lot of that work in the last several years. And then the deep learning algorithms, those have also been around for decades. But what we are finding right now is that the algorithms and the hardware, the technologies available have hit that perfect point along with industry's interest with the amount of data we have to make it more what we would call mainstream, right? Where more and more people are talking about it. Every resume you see has AI and deep learning written on it. So it's not that AI is something net new and deep learning something completely net new. It's that today's technologies allow us to use them. And because we have a lot of experience doing really elaborate solutions, doing complicated solutions, it was a national fit to develop Dell EMC's AI deep learning machine learning solutions in this lab. Right, so that's a really interesting point that the tipping point of all these technologies seems to be happening at the same time. So we've got really fast CPUs. We've got GPUs now coming on scene. We were just at the Google Cloud, so they talk about TensorFlow, PUs, TPUs. So a lot of action there. A lot of excitement on the networking side. Of course, 5G is coming on the mobile space in a very short period of time, which is a total game changer. And you guys are really playing it. And then of course, I forget to mention, in a solid state drives and getting away from spinning disk, which really opens up another level of performance. But it's funny, because all systems, they ultimately find the bottleneck. So as you've seen the evolution of all these different pieces, how have you seen that bottleneck move and how the elimination of all those bottlenecks enabled some of the solutions you guys are working on today? So you're absolutely right. So when we talk about systems or when we talk about solutions for high performance computing or AI, we're not talking about, oh, you know, here's this GPU card or here's this Xeon CPU and this is the best thing that you need, right? Our job in this lab, our goal is to bring new technologies into the lab from inside of Dell, as well as from all our partners and evaluate those new technologies, see how they fit well together. Because the final solution is comprised of multiple different pieces coming together and being interoperable. So putting these new technologies together, vetting them out to see which one's ready for the market, which one still needs more work in a proof-of-concept phase, putting these things together, designing systems, building them, doing evaluations. So doing benchmarks, running applications, doing a whole bunch of best practices and tuning and doing this not just with all our partners but also with our customers. So you know, this lab is set up for remote access for customers. Putting all of this together to find the right solution, not just for toy data sets, but for real-world use cases. So you might find that you have use cases where you do not need the highest speed interconnect or you do not need the fastest CPU or the best GPU and you need a balance, say, with memory bandwidth or solid-state device or NVMe, like you're saying. And our charter is to build the right solutions for specific workloads. And that's what we do here. Right, so it's a really interesting take because you build it for benchmarking, right? Everybody wants a good benchmark. So you can build on optimum solutions, but ultimately you want to build industry solutions and then even subset of that, you invite customers in to optimize for what their particular workflow or their particular business case, which may not match the perfect benchmark spec at all, right? That's exactly right. And so that's the reason this lab is set up for customer access because we do the standard benchmarking but you want to see, you know, what is my experience with this? How does my code work? And it allows us to learn from our customers, of course, and it allows them to get comfortable with Dell Technologies to work directly with the engineers and the experts so that we can be their true partners and trusted advisors and, you know, help them advance their research, their science, their business goals. Right, and then as you said, and it's not only kind of the cutting-edge stuff, whether it's new CPUs or GPUs, but it's also all the kind of minutia that makes a rack a rack. It's all the connectors and all these things that can have a fail if they're not properly spec'd or they become that unfortunate bottleneck. So you guys built the whole rack out, right? Not just the fun, shiny new toys. Yeah, you're right. So typically, you know, when something fails, it fails spectacularly, right? So I'm sure you've heard horror stories where there was equipment on the dock and it wouldn't fit in the elevator or things like that, right? So there are lots of other teams that handle, of course, Dell's really good at this, you know, the logistics piece of it, but even within the lab, when you walk around the lab, you'll see our racks are set up with power meters. So we do power measurements. Whatever best practices and tuning we come up with, we feed that into our factory. So if you buy a solution, say, targeted for HPC, it would come with different BIOS tuning options than a regular, say, Oracle database workload. We have this integration into our software deployment methods. So when you have racks and racks of equipment or one rack of equipment or maybe even three servers and you're doing an installation or all the pieces are baked in already and everything is easy, seamless, easy to operate. So our idea is the more that we can do in building integrated solutions that are simple to use and performant, the less time our customers and their technical computing and ID departments have to spend worrying about the equipment and they can focus on their unique and specific use case. Right. And then the other little piece that you didn't mention but really important piece of the puzzle is you guys have a services arm as well. So you can take the time, spec it out and then you actually have services capability to help actually implement it, hook it up, connect it to their data sources, do the integrations, et cetera, which can't forget about that piece. You're absolutely right. We're an engineering lab, which is why it's really messy, right? Like if you look at the racks, if you look at the work, we're a working lab, we're an engineering lab, we're a product development lab and of course we have a support arm, we have a services arm and sometimes if we're working with net new technologies, we conduct training in the lab for our services and support people. But we're an engineering organization and so when customers come into the lab and work with us, they work with it from a engineering point of view, not from a pre-sales point of view or a services point of view. I'm just curious, how long are some of those engagements when a team of customer engineers comes to work with you guys, say on a specific iteration of a solution that you built? Is that week long process, days process, how long do those kind of engagements typically take? Right, so we set up typically for remote access. Sometimes we'll have a customer saying, hey, can we come over for a week and spend time with you, speak to the different engineers. So sometimes the engagements are as short as two or three days because they know exactly what they wanna do, they want us to set up an account and they wanna run tests and then we discuss and analyze results. Sometimes it can be as long as three or four weeks, depending on the scope of the project. And sometimes it's not just the customers logging in and doing stuff, it's us working with them or our team running their codes. So then there's more back and forth as well. So it's interesting, so the scope of today is all talking about the AI portion of the lab. But as you said, you've had this for HPC and you've got a bunch of kind of core, I don't wanna call it old school, but old school infrastructure apps, you've got Oracle in here running, you've got SAP running. So how do you, kind of what's the benefit of having the experience in this broader set of applications as you can apply it to some of the newer, more exciting things around AI, machine learning, deep learning. Right, so the fact that we are a shared lab, right? Like the bulk of this lab is high performance computing in AI, but there's lots of other technologies and solutions we work on over here and there's other labs in the building that we have colleagues in as well. The first thing is that the technology building blocks for several of these solutions are similar, right? So when you're looking at storage areas, when you're looking at Linux kernels, when you're looking at network cards or solid state drives or NVMe, several of the building block technologies are similar. And so when we find interoperability issues, which you would think that, there would never be any problems, you'd throw all these things together, they always work like, of course. Right, so when you sometimes rarely find an interoperability issue, that issue can affect multiple solutions. And so we share those best practices because we engineers sit next to each other and we discuss things with each other, we're part of the larger organization. Similarly, when you find tuning options and nuances and parameters for performance or for energy efficiency, those also apply across different domains. So while you might think of Oracle as something that's been done for years, with every iteration of technology, there's new learning, and that applies broadly across anybody using enterprise infrastructure. Right, so I've just loved to get your perspective as you come to work every day. What excites you to take what was, if not the domain exclusively of big universities and feds, but a lot of it was there, to start to apply AI, machine learning, to such a broad swath of applications. What gets you excited? What are some of the things that you see like, I'm so excited that we can now apply this horsepower to some of these problems out there. Right, so that's a really good point, right? Because most of the time when you're trying to describe what you do, it's hard to make everybody understand, well, not in what you're doing, right? But sometimes with deep technology, it's hard to explain what's the actual value of this. And so a lot of what we're doing in terms of exascale, it's to grow the human body of knowledge forward, to grow the science happening in each country, moving that forward. And that's kind of at the higher end when you talk about national labs and defense and everybody understands that needs to be done. But when you find that your social media is doing some face recognition, everybody experiences that and everybody sees that. And when you're trying to describe the, we're all talking about driverless cars or we're all talking about, oh, it took me so long because I had this insurance claim and then I had to get an appointment with the appraiser and they had to come in. I mean, those are actual real world use cases where some of these technologies are going to apply. So even industries where you didn't think of them as being leading edge on the technical forefront in terms of IT infrastructure and digital transformation, in every one of these places, you're going to have an impact of what you do, whether it's drug discovery, right? Or whether it's next generation gene sequencing or whether it's designing the next car, like pick your favorite car, or when you're flying in an aircraft, the engineers who were designing the engine and the blades and the rotors for that craft were using technologies that you've worked with. And so now it's everywhere, everywhere you go we talked about 5G and IoT and edge computing. I mean, we all work on this collectively. So it's our world. Okay, so last question before I let you go. Just having the resources to bear in terms of being in your position to do the work when you've got the massive resources now behind you of Dell, the merger of EMC, all the subset brands, Isilon, so many brands, how does that help you do your job better? What does that let you do here in this lab that probably a lot of other people can't do? Yeah, exactly. So when you're building complex solutions, there's no one company that makes every single piece of it, but the tighter that things work together, the better that they work together, and that's directly through all the technologies that we have in the Dell Technologies Umbrella and with Dell EMC, and that's because of our super close relationships with our partners that allows us to build these solutions that are painless for our customers and our users. And so that's the advantage we bring, this lab and our company. All right, Grimu. Well, thank you for taking a few minutes, your passion shines through. Thank you. All right, she's Grim, I'm Jeff. We are at the Dell EMC High Performance Computing and Artificial Intelligence Innovation Labs. Thanks for watching.