 Welcome back to theCUBE's coverage of Supercomputing Conference 2022, otherwise known as SC22, here in Dallas, Texas. This is day three of our coverage, the final day of coverage here on the exhibition floor. I'm Dave Nicholson and I'm here with my co-host, tech journalist extraordinaire, Paul Gillum. How's it going, Paul? Hi, Dave, it's going good. And we have a wonderful guest with us this morning, Dr. Panda from The Ohio State University. Welcome, Dr. Panda, to theCUBE. Thanks a lot. Thanks a lot. Paul, I know you're chomping at the bit. You have incredible credentials, over 500 papers published. The impact that you've had on HPC is truly remarkable, but I want to talk to you specifically about a project you've been working on for over 20 years now called Mvapage, high performance computing platform that's used by more than 3,200 organizations across 90 countries. You've shepherded this from its infancy. What is the vision for what Mvapage will be, and how is it a proof of concept that others can learn from? Yeah, Paul, that's a great question to start with. I mean, I started with this conference in 2001. That was the first time I came. It's very coincidental. If you remember the InfiniVan Networking Technology, it was introduced in October of 2000, okay? So in my group, we were working on MPI for Marinette Quadrics. Those are the old technology, if you can recollect. When InfiniVan was there, we were the very first one in the world to really jump in. Nobody knew how to use InfiniVan in an HPC system. So that's how the Mvapage project was born. And in fact, in supercomputing 2002, on this exhibition floor in Baltimore, we had the first demonstration. The open source Mvapage actually is running on an eight node InfiniVan cluster. Eight, no zeros. And that was a big challenge. But now over the years, I mean, we have continuously worked with all InfiniVan vendors, MPI forum. We are a member of the MPI forum and also all other network interconnect. So we have steadily evolved this project over the last 21 years. I'm very proud of my team members working nonstop, continuously bringing not only performance, but scalability. If you see now, InfiniVans are being deployed in 8,000, 10,000 node clusters. And many of these clusters actually use our software stack, the Mvapage. So we have done a lot of our focuses like we first do research, because we're in academia, we come up with good designs, we publish, and in six to nine months, we actually bring it to the open source version and people can just download and then use it. And that's how currently it's being used by more than 3,000 organizations in 90 countries. But the interesting thing is happening in your second part of the question. Now as you know, the field is moving into not just HPC, but AI, big data, and we have those support. This is where we look at the vision for the next 20 years. We want to design this MPI library so that not only HPC, but also all other workloads can take advantage of it. We've seen libraries have become a critical platform supporting AI, TensorFlow and the PyTorch and the emergence of some sort of default languages that are driving the community. How important are these frameworks to the development of the progress, making progress in the HPC world? Yeah, no, those are great. I mean, SpyTor, TensorFlow, I mean, those are now the bread and butter of deep learning machine learning, am I right? But the challenge is that people use these frameworks, but continuously models are becoming larger. You need very fast turnaround time. So how do you train faster? How do you do inferencing faster? So this is where HPC comes in. And what exactly what we have done is actually we have linked TensorFlow PyTorch to our MAPHitch. Because now you see that if the MPI library is running on a million core system, now your PyTorch and TensorFlow can also be scaled to those large number of cores and GPUs. So we have actually done that kind of a tight coupling and that helps all the AI researchers to really take advantage of HPC. So if a high school student is thinking in terms of interesting computer science, looking for a place, looking for a university, Ohio State University, world-renowned, widely known. Talk about what that looks like on a day-to-day basis in terms of the opportunity for undergrad and graduate students to participate in the kind of work that you do. What does that look like? And is that a good pitch for people to consider the university? Yes, I mean we continuously from a university perspective, by the way, the Ohio State University is one of the largest single campus in the US. One of the top three, top four, we have 65,000 students. It is one of the very largest campus and especially within computer science where I am located, high-performance computing is a very big focus. And we are one of the, again, the top schools all over the world for high-performance computing. And we also have very strengthened AI. So we always encourage the new students who would like to really work on top of the art solutions, get exposed to the concepts, principles, and also practice, okay? So we encourage those people that wish you can really bring you those kind of experience. And many of my past students, staff, they're all in top companies, now have become all big managers. How long did you say you've been at? 31 years. 31 years. So you've had people who weren't alive when you were already doing this stuff. That's correct. They then were born. Yes. They then grew up. Yes. Went to university, graduate school, and now they're on. Now they're in many top companies, national labs, all over the universities, all over the world. So they've been trained very well. You've touched a lot of lives, sir. Yes, thank you, thank you. We've seen really a burgeoning of AI-specific hardware emerge over the last five years or so. And architecture's going beyond just CPUs and GPUs, but to ASICs and FPGAs and accelerators. Does this excite you? I mean, are there innovations that you're seeing in this area that you think have great promise? Yeah, there is a lot of promise. I think every time you see now super computer in technology, you see there is sometimes a big barrier comes. A barrier or a jump, rather I'll say. New technology comes, some disruptive technology, then you move to the next level. So that's what we are seeing now. A lot of these AI chips and AI systems are coming up, which takes you to the next level. But the bigger challenge is whether it is cost effective or not. Can that be sustained longer? And this is where commodity technology comes in, which commodity technology tries to take you far longer. So we might see like all these like Cerebras, Gaudi, a lot of new chips are coming up. Can they really bring down the cost? If that cost can be reduced, you will see a much more bigger push for AI solutions which are cost effective. What about on the interconnect side of things? Obviously, your start sort of coincided with the initial standards for InfiniBand. Intel was really big in that architecture originally. Do you see interconnects like RDMA over converged Ethernet playing a part in that sort of democratization or commoditization of things? What are your thoughts there for Ethernet? No, no, this is a great thing. So we saw the InfiniBand coming. Of course, InfiniBand's commodity is available, but then over the years, people have been trying to see how those RDMA mechanisms can be used for Ethernet, and then Rocky has been born. So Rocky has been also being deployed. But besides these, I mean, now you talk about Slingshot, the crazy Slingshot, it is also an Ethernet based systems, and a lot of those RDMA principles are actually being used under the hood. So any modern networks you see, whether it is InfiniBand, Rocky, Slingshot, Cornelius Network, Rockport Network, you name any of these networks, they are using all the very latest principles. And of course, everybody wants to make it commodity, and this is what you see on the show floor. Everybody's trying to compete against each other to give you the best performance with the lowest cost, and we'll see whoever wins over the years. Sort of a macroeconomic question. Japan, the US, and China have been leapfrogging each other for a number of years in terms of the fastest supercomputer performance. How important do you think it is for the US to maintain leadership in this area? Big, big thing, significantly. We are seeing that, I think for the last five to seven years, I think we lost that lead, but now with the frontier being the number one, starting from the June ranking, I think we are getting that leadership back. And I think it is very critical, not only for fundamental research, but for national security, trying to really move the US to the leading edge. So I hope US will continue to lead the trend for the next few years until another new system comes out. And one of the gating factors there is the shortage of people with data science skills. Obviously, you're doing what you can at the university level. What do you think can change at the secondary school level to prepare students better for data science careers? Yeah, I mean, that is also very important. I mean, we always call it like a pipeline, you know? That means when PhD levels we are expecting like this, even we want to students to get exposed to many of these concerts from the high school level and things are actually changing. I mean, these days I see a lot of high school students, they know Python, how to program in Python, how to program in C, object oriented things, even they are being exposed to AI at that level. So I think that is a very healthy sign. And in fact, we, even from Ohio State side, we are always engaged with all these K to 12 in many different programs and then gradually trying to take them to the next level. And I think we need to accelerate also that in a very significant manner because we need those kind of a workforce. It is not just like a building a system, number one, but how do we really utilize it? How do we utilize that science? How do we propagate that to the community? Then we need all these trained personnel. So in fact, in my group, we are also involved in a lot of cyber training activities for HPC professionals. So in fact, today there is a buff at 115. Yeah, I think 1215 to 115. We'll be talking more about that. About education. Yeah, cyber training, how do we do for professionals? So we had a funding together with my co-PI, Dr. Karen Tomko from Ohio Supercomputer Center. We have a grant from National Science Foundation to really educate HPC professionals about cyber infrastructure and AI. Even though they work on some of these things, they don't have the complete knowledge. They don't get the time to learn and the field is moving so fast. So this is how it has been. We got the initial funding and in fact, the first time we advertised in 24 hours we got 120 application, 24 hours. We couldn't even take all of them. So we are trying to offer that in multiple phases. So there is a big need for those kind of training, sessions to take place. I also offer a lot of tutorials at all different conference. We had a high-performance networking tutorial here. We have a high-performance deep learning tutorial, high-performance big data tutorial. So I've been offering tutorials even at this conference since 2001. So in the last 31 years, the Ohio State University, as my friends will remind me, it is properly called, you've seen the world get a lot smaller because 31 years ago, Ohio and this, you know, roughly in the middle of North America and the United States was not as connected as it was to everywhere else in the globe. So that's, I kind of boggles the mind when you think of that progression over 31 years. But globally, and we talk about the world getting smaller, we're sort of in the thick of the celebratory seasons where many groups of people exchange gifts for varieties of reasons. If I were to offer you a holiday gift that is the result of what AI can deliver the world, what would that be? What would the first thing be? This is like the genie, but you only get one wish. I know, I know. So what would the first one be? Yeah, it's very hard to answer one way, but let me bring a little bit different context and I can answer this. I talked about the MAPI project and all, but recently, last year, actually we got awarded an NSFAI Institute Award. It's a $20 million award. I am the overall PI, but there are 14 universities involved. And who is that institute? What does that say? Icicle, I-C-I-C-L-E, Icicle. You can just do icicle.ai. And that lies with what exactly what you are trying to do, how to bring a lot of AI for masses, democratizing AI. That's what is the overall goal of this institute. Think of like a, we have three verticals we are working. Think of like one is digital agriculture. So that would be my like the first wish. How do you take HPC and AI to agriculture? The world as though we just crossed eight billion people. And that's right. We need continuous food and food security. How do we grow food with the lowest cost and with the highest yield? Water consumption. Water consumption. Can we minimize or minimize the water consumption or the fertilization? Don't do blindly. Technology's out there. Like let's say there is a big field. A traditional farmer see that, yeah, there is some disease. They will just go and spray pesticides. It is not good for the environment. Now I can fly a drone, get images of the field in the real time, check it against the models, and then it will tell that, okay, this part of the field has disease one. This part of the field has disease two. I indicate to the tractor or the sprayer saying, okay, spray only pesticide one year, pesticide two year. That has a big impact. So this is what we are developing in that NSF AI Institute. icicle.ai, we also have, we have chosen two additional verticals. One is animal ecology, because that is very much related to wildlife conservation, climate change. How do you understand how the animals move? Can we learn from them and then see how human beings need to act in future and the third one is the food insecurity and logistics, smart food distribution. So these are our three broad goals in that Institute. How do we develop cyber infrastructure from below, combining HPC AI, security. We have a large team, like I said, there are 40 PIs there, 60 students, we are a hundred member team, we are working together. So that will be my wish. How do we really democratize AI? Fantastic. I think that's a great place to wrap the conversation here on day three at Super Computing Conference 2022 on theCUBE. It was on our Dr. Panda working tirelessly at the Ohio State University with his team for 31 years, toiling in the field of computer science and the end result, improving the lives of everyone on earth. That's not a stretch. If you're in high school thinking about a career in computer science, keep that in mind. It isn't just about the bits and the bobs and the speeds and the feeds. It's about serving humanity. Maybe a little too profound a statement, I would argue not even close. I'm Dave Nicholson with theCUBE, with my co-host Paul Gillin. Thank you again, Dr. Panda. Stay tuned for more coverage from theCUBE at Super Compute 2022 coming up shortly. Thanks a lot.