UT08x08: Spreading Standards and Ideas with Dr. J Metz

Standards bodies don't just set technical specifications. They also drive greater understanding of the topics in enterprise technology, like storage and ai. This episode of Utilizing Tech brought to you by soddy features Dr.

J Metz, chair of Snia A and the Ultra Ethernet Consortium and Technical Director of a MD, talking about how we can spread standards and ideas around the industry. Welcome to Utilizing Tech, the podcast about emerging technology from Tech Field Day, part of the Futurum Group. This season is presented by solidi and focuses on advanced topics like AI at the edge and related technologies.

I'm your host, Stephen Foskett, organizer of the Tech Field Day events series, and joining me today from Soy as my co-host is my old friend, Scott Shaley. Welcome, Scott. Hey, Stephen.

Good to see you again. Uh, I know we recently saw you in person, actually at a, a recent field day event. That was awesome.

Uh, great to be here again. Yeah, it's good to, it's good to see you as well. And um, as I said, you know, you, you and I go way back.

Um, one of the areas that we've both focused on for a long, long time is storage. And, you know, those of you outside the storage industry may not know it, but there's actually a really nice community in storage, um, a great group of people who love to get together at events. You know, for example, the storage developer conference is one of my favorite nerd fests, you know, FMS, there was the old storage conferences and so on.

And, and one of the coolest things about that is sort of the cross pollination that happens when you learn about what other companies and other people are working on, and you try to bring that into your world. Yeah, absolutely. I mean, that's a, a perfect example of that is, uh, I have chosen my, uh, attire appropriately as I now sit on the board of directors of said one of those groups, snia a and it's been an interesting way to utilize, to your point, what's going on in the market around, you know, all of us working to drive innovation forward, but we all know that we can't just do it as a one-off.

We can't be unique or independent in certain ways and aspects of that. And so being able to tie all that together and, and bring it in as a conglomerate of people and companies to present to our customer base makes, uh, the existence of all these technologies more possible. Yeah, it's been really, um, great having, uh, you know, I think people, when they think about standards bodies, they think about the technical aspect of standards bodies rather than the sort of evangelical and spreading the gospel aspect of standards bodies.

Uh, SNIA a is one of the companies or one of the organizations, I'm sorry, that is really, I think, spreading the gospel of storage far and wide. It's not about, you know, totally looking inward at, you know, how can we make storage better? It's about how can we bring storage to the world?

And that's why we have, uh, brought our old friend, uh, from who, who happens to know a thing or two about Snia. Uh, Dr. Jay Metz to join us here on the, uh, uh, podcast.

Welcome to the show, Jay. Thank you very much. Very happy to be here.

Thank you. So tell us a little bit about yourself. Well, I am a technical director for advanced storage and networking strategy for the data center group inside of a MD.

And I am also, as you said, I'm the chair of SNA, uh, have been for about five years now. And I'm also the chair of the alters in that consortium. So I've got kind of hands and and feet in both areas of the networking and the storage and the storage networking and so on and so forth.

Uh, so that's what I do that and wear hat Wear. It's, yeah, I was gonna say, and of course, he's wearing the, uh, traditional J Mets hat attire that is known for far and wide, uh, is, uh, Steven mentioned you'd go to an SDC event, and if you see Jay without his hat, you don't know who he is. So it been, Well, if I take off the hat, it's a, it's a being incognito, you know, people, I would never, ever make fun of Superman, you know, for taking off his glasses because I take off the hat and people just don't know I'm there.

It's, it's a fascinating event. And now speaking of not knowing that you're there, um, that's one of the things that people take for, take advantage of, if you will, as, as far as what this conversation's about, which is how we participate in these standards and these different consortiums and what it's really doing for the market and for our customers. And a lot of the time, those folks don't really see what this behind the scenes does.

So to, to Steven's point, the, um, evangelical part of it or the, the communication piece of it is something that you and I have spent a lot of time doing together, uh, within Ssea a now you as part of UBC as well. So, Yeah, and, and it's always, it was always kind of interesting because, you know, from the outside looking in, you think that this is just this one big monolithic entity that's kind of, um, solving a particular problem. But, uh, it winds up being much more involved than that with different, you know, perspectives and different attitudes and different thoughts and ideas and creative, uh, creative venues.

But it winds up being particularly interesting for those of us who get involved in this worship because there's always a new problem that has to be solved. You know, there's, there's nothing is ever really finished. So we get, we get some pretty nuanced approaches to solving problems that get both big and very small and, uh, in, in different areas.

That's a good, uh, kind of summary of, of storage. One of the things that makes storage interesting is the fact that it is very big and very small and very fast and very slow, and very high powered and very low powered. I mean, storage is a lot of things.

Mm-hmm. And, and, you know, I, you and I, and well, three of us are really have been, um, discussing all of these aspects of this technology for so long. And yet, um, I think, again, there's not as much understanding of the nuances of storage, but that's changing now that we are starting to have greater and greater demands.

I mean, AI drives, um, greater use, uh, or greater collection of data. It requires data to make it work. Um, a AI-based applications are very data-driven applications, and data collection means storage.

It drives storage capacity, it drives storage performance, it drives bandwidth, it drives connectivity and networking and io and these are all things that groups like Snia a and the Ultra Ethernet Consortium are trying to evolve. Right. I mean, have you noticed, and I'm gonna ask you a leading question here, have you noticed that these things are ever more in demand in the modern world?

Uh, you know, what we've been doing in storage is suddenly, uh, really important. Well, I think the short answer is yes, of course. Um, the, the, the longer answer is a little bit more involved because the three of us and those who are storage oriented people, we've got a certain intuition when it comes to what we mean when we use the term for those that are maybe software oriented or compute oriented or hardware oriented.

They may not see storage the way that we do intuitively, right? They may see it as capacity inside of a drive or a hard drive in and of itself that is storage after all. But the, but the, the real element that we think about when we talk about this is that it's not just about how it's stored.

It's about how, how you get it back. How do you move it, how do you preserve it? How do you, uh, make sure that it is there when you need it, when you need it at the right place where you need it, how do you make sure it is the, the exact thing that you thought you were going to get?

Um, and so what winds up happening is that what we consider to be storage, other people would call memory, other people would call networking, other people would call software. Other people would call, you know, backups or archiving. Other people might actually look at it and say, um, you know, it's, it's management, right?

So all of these things are, are depending upon how you take the diamond and you look at the different facets, and you could see storage reflected back at you at any given point in time. But you know, what I think we try to do in snea, and what we're trying to do in UEC and some of the other organizations that we work with, is that we're trying to say, look, if, if you, you say that you do this, or you need this kind of, or that kind of bandwidth, or you need this kind of latency, what do you need it for? Well, you need it for getting data from one place to another place, or what does that entail?

And the further down that rabbit hole you go, the further there is to go, it's sort of like saying, what is the length of the English coastline? As you start to drill down until the beaches and the actual individual grains of sand, you start to realize that it is an ever increasing fractal distance of information that you're gonna have to try to consume. Storage is the same way.

If you wanna talk about moving a bit from here to there, what does that actually mean? Does that mean you have to go through buses? Does it mean you have to go through buffers and caches and, and NAND or, uh, you know, high bandwidth memory?

What does it actually mean? You know, what are the channels that you use? What are the networks that you use?

All these makes a difference, and it makes a difference at a really small level, and it, it makes a difference at a really big level. So, for instance, you know, um, I, I know recently you had a conversation with Gary Greer. He deals with really big systems, right at Atlanta, really enormous systems.

I mean, he's got petabytes of, of ram, for example. Um, and then at the same time you've got these, you know, these AI models that are starting to approach the trillions of parameters. And the, the average lay person doesn't understand what that means in terms of having the material to be able to do this.

If I've got a 1 trillion parameter, large language model, for example, I need around 32 terabytes of RAM just to be able to hold it, right? Well, no processor has 32 terabytes of ram, which means you've gotta be able to have a lot of processors together working together as one unit. Well, how do you do that?

The data has to move from one place to the other, and you create little tricks, right? You create parallelism and little tricks. That means that I have things going at the same time.

But what do you do when you do that? Well, you change the nature of the data movement. It's no longer big pieces of data moved all at once.

It's now big pieces of data moved all at once, and little pieces of data moved all at once to let everybody know where the big pieces of data are. So you've got a, uh, you're, you're moving, you know, the needle and a number of different areas at the same time, just by small little tweaks in what you're trying to accomplish. And storage has to embrace all of it.

And so that's why what we do at NIA is so interesting 'cause we have to do all of it. Yeah. And, and if you look at it from that perspective, I find one of the unique pieces of that is like, look at the, the history of soy and where the markets come from.

We had the introduction of E-D-S-F-F and the small form factor, which is now SFF within SNA, its whole purpose is to help with connectivity, what the box looks like and things like that. And we've got new systems and solutions that are being utilized by even the, the fastest AI clusters. Now they're using a unique form factor designed and developed out of something like Snia A and led by a bunch of these different companies paying attention to what you're talking about.

Um, I have a, a very dear friend in the virtualization world, and I think I may have or not used this, uh, previously, but he still talks about storage as chips and I, and we're talking SSDs that sit at 64 terabytes. And to him, it's still just a chip because he's not a, he's not a hardware guy. And so that's where we, as these groups of people who really know what he's talking about, can help evolve that ecosystem without him really having to change his definition, but just solve this problem.

I don't wanna cause any face palming, but I think a lot of people still think of storage as discs or maybe even as tape. And, um, of course those are still, they are still relevant technologies. And I think that if you ask somebody in the know about storage, uh, they will absolutely stand up for disc and tape, but they will also say, check out what we're doing here.

You know, And, and, and as well, they should, right? Because, because the thing is, like I said at the very beginning, we understand and intuit what it actually means, right? And so, but when I talk to the people at UEC, for example, or I go in, I across the aisle to OCP, or if I talk to people at the Linux Foundation, I have to choose my words more carefully because there's the shortcut of saying storage doesn't exist with them, right?

The, you know, incredibly intelligent people, but from it coming from a different perspective. So it's really important that we allow ourselves the luxury of learning how to communicate the proper terms in a way that's gonna be received correctly, right? So, um, when I, when I want to talk about the data for ai, or if I wanna talk about the data for, um, for HPC, that gives me the ability to have a level, common ground of when I'm talking topologies and networks, or if I'm talking about bid error rates on the physical level, or if I'm talking about, you know, in-network collectives at the software level.

Because by being able to redefine the question, you have a lingua franca that you can use to be able to associate what they want with what you can provide and vice versa. Yeah. And you actually bring up a very interesting, uh, comment about the fact that the, the cross pollination, right?

So I sit on snea board alongside yourself, but I also participate in N vme and a few of the other things like OCP whatnot. And not only do you have to choose the, the, the moniker, the terms you use, but you also have to be careful about overshare and unders share and stuff. 'cause there's all these nuances, just like you have NDAs between companies, you have relationships between consortiums and until something's officially official, you actually have some unique aspects of how managing all of this stuff comes into play.

And being able to address that and drive across, uh, those barriers and screw driving solutions is kind of a, a unique aspect of what we get to do from that side of our, our day job. If you'll, Yeah, and I think it's absolutely critical. I mean, you know, last month we had a, a joint session between UEC and ssa, right?

We had a, you know, a face-to-face between the two different organizations. We, we did technical symposium individually, and then in the evenings we had birds of a feather where we could kind of combine and, and work. And then of course, we had the regional STC, um, where the, you know, the materials should be available online, um, soon if, if they're not already.

Uh, and the whole, the whole purpose here is to make sure that, that the work that's being done can be extended into other areas so that people don't have to reinvent that wheel. UEC is one of those places in particular that is, that is specifically, um, going to hurt, um, with the, in the data area if it's not careful, right? Um, and the, this is not necessarily just A-A-S-N-A solution either, but the SNA group, um, needs to be able to understand the proper, you know, framework under which the work that they're doing is.

And so mixing the two initiatives together is really important. If I want to, so for example, in ai, we have multiple network types. We've got a front end network, which is your general purpose network, and that's where most of the storage lives.

Then you have a purpose for ai, right? You have a purpose built network for ai, and that's what we call a backend network. It's like the 32, you know, uh, terabytes of RAM that you need in order to get the, you know, a system to work, uh, for model.

But since you need the data on one network, but the data exists on the other network, you wanna make sure that you have the best and most efficient ways of transferring the data. Or in the future, you wanna have the data on the network that's doing the processing, which right now it isn't. So that's a, that's a, a fundamental aspect of AI that most don't even realize, right?

They don't realize that the data isn't even where the processors are, that just doesn't exist. So you have to go through all kinds of unnatural acts to get the data into the proper network so that the GPUs and the TPUs and the accelerators can actually process it. And then if you're doing a lot of that movement, it makes sense that the organizations that are focusing on the AI network and the organizations that are doing the storage talk, and that's the whole reason why we're doing what we're doing.

Yeah. You bring up a, that's a very interesting, and, and to your point, as I, as I mentioned, we recently saw Steven in person, and one of the, the aspects of the presentation that we brought forward was working with a partner to highlight how we can leverage storage to actively replace aspects of the memory required to drive some of those language models. And there's trade-offs.

Do I spend a fortune on the RAM to add and use my storage and accept a little bit of those trade-offs and things like that? But those are things that these organizations can't take the time to think about if they're busy thinking about that true backend that we're driving with the consortiums and, and the work that we're doing to get data from here to there, whether it's edge in, you know, AI at the edge, or AI in the data center, or rack to rack, all that kind of stuff plays a very big part of what we kind of drive with all these different, uh, efforts that we're doing. Well.

I've always been a big fan of one plus one equals three. Um, and I've used that phrase before, but if you, if you look at a particular technology like we've got inside of, of CN, there's two in particular that come to mind that are, are, and not only two, but there's just two in particular that come to mind. One is the smart data accelerator interface, and that is the ability to use hardware to do movement, uh, data movement from, uh, from one memory location to another memory location.

Um, and in and of itself, it is a very interesting approach to solving problems in a, um, uh, especially in a high, highly abstracted area, like, you know, something as a service. Um, the other thing that's really important is computational storage, right? Where you've got the compute processing power next to the storage device itself in and of itself, it is an interesting technology.

Now, let's switch over to, to UEC for example. I've got a processor that needs to run an awful lot of data, and I need to move the data in, but the data has different types. It's got object types, it's got parallel file systems, it's got block, and not all are being used at any given point in time.

But if I, like in, in, um, you know, in LA's situation, they managed to figure out a way to avoid having to move exabytes of data or, um, at any given point in time for their iterative cycle inside of their, their workload by doing processing, right? At the drive itself. In U'S terminology, we could do the same thing because the parallelism that involves really means that I've gotta send data to another processor to be processed.

But if I wind up with that same principle of having the compute next to the data, I now shut down the need for the bandwidth, the need for the latency, the need for, uh, the network, uh, connectivity at that level. I can actually reduce the need of it, not, you can't reduce it to completely, you can't eliminate it, but you can reduce the need of it because these things are getting so big that you find yourself at the limits of physics, right? We need better efficient ways of doing things.

So what's that? That is the, the memory movement model, the processing near the me, uh, near the, uh, near the, the data itself. And you've got the proper network directly connected into, uh, the processors, one plus one equals three.

So in, in and of itself, it's great combined to get, it's even more powerful getting those two groups to get, come together and have those conversations, has to start somewhere. And so that's what we did last month. And then we're gonna continue to do this, uh, in the future, not just with between UEC and and SNA, but you know, um, organizations like OCP, like IEE, um, you know, UA link, uh, OFA, the, all of these different groups and organizations, that's just, some of them, you know, they all have a vested interest as we move forward in solving these massive problems to be able to be aligned.

And fortunately, it's a really good thing, um, that right now, as of right now, there's a, a willingness and an inclination to do so. You know, it's interesting that you talk about that. Um, if I had, um, taken out all of the proper nouns from what you just said, you just described the Edge AI use case as well, where we have limited bandwidth, we have to move processing closer to the data, et cetera.

I mean, and this to me, Jay, this is the thing that has been so remarkable about going to industry standards, bodies and, and events and seeing what they're developing. It's the, maybe unintended, but always in fun and surprising cross pollination of ideas. I think when, when, um, the next generation storage form factor designs were set, no one could have predicted how that would transform the design, the physical design of edge servers.

And yet it did. And it is rapidly changing the entire edge industry when, um, technologies, you know, DMA technologies and, um, you know, mo computational storage and so on, I think that those were defined before, uh, the AI use case arose, or at least the AI training use case arose. And yet, look at that.

That's pretty useful over there, you know, and that's something that makes this all happen. It's like, um, it's like magic happens when you have different people with different areas of expertise all communicating openly and all saying, Hey, wait a second, we did something really like this back in the day to solve this other problem, and now we have a similar problem here. What if we, you know, for example, move compute closer to the collection and storage of data?

What if we, you know, offload things? What if we develop protocols, uh, that allow you to, um, to have, for example, hierarchical memory, you know, that came from, uh, supercomputing, uh, developing pneuma supercomputers, and then suddenly that's the core technology that enables CXL or the core concept at least that enables CXL. It's so interesting to see how these things kind of spread, like fire from one spot to the other spot.

And, um, and I think we're definitely seeing that with HPC and AI right now, right? Yeah, yeah. There's, there's a lot of characteristics of both that crossover, but enough of a difference to make it interesting.

Um, so yeah, it's, one is not exactly a complete overlap of those Venn diagrams on the other, but there's, like I said, there's enough of a, a cross combination to really start to challenge people into solving those problems. I think, um, you know, when we get into, when we get into what's going on in the future, the very cool parts of this is that much of what we want to do has been solved in the past. It's at a different scale.

It's at a different level, but it involves, you know, two basic fundamental, uh, concepts. One is the margin for error is much smaller, right? So, and, uh, and as a result, very small increments of, um, uh, problems can create much larger amplification problems in, in, you know, for systems, right?

Um, because of the tightly coupled nature of the way that these components work, if one goes down, the whole thing goes down. So it's very, very delicate, very sensitive. So creating robustness and reliability in there is something of a challenge.

But the other part of it is time. And we tend to forget that as fast as we are, we are still dependent upon other things to be able to feed us information or we have to feed them information. And sometimes that information is at a higher level.

It's at a control plane level, it's at a management level. Sometimes it's the data plane itself. But the act of time has made a huge, played a huge role in what we actually need to do, right?

And that's where the bandwidth comes into play. That's where the latency comes into play. The ability to get something or send something very quickly also means that you now have to run the risk of being idle while these other things are doing their own thing, right?

So the whole system has to rise. All, all the boats have to lift, right? That water has to be able to lift all boats.

It's not good enough to say that I've got the world's best GPU or the world's best CPU or the world's fastest ram if I can't get data in and out of it fast enough, or if I can't get the other piece of equipment on the other end of the wire to send or receive fast enough. And we're talking about wildly disparate components. We're talking about network interface cards.

We're talking about, you know, GPUs, we're talking about TPUs and so on and so forth. And they're not all equal. They don't all have the same performance characteristics.

And so, and they're not designed to. So you've got a lot of variability and a lot of ambiguity when you start off with a very specific and demanding kind of workload, whether it be HBC or ai, that all has to work together in concert. You know, you can't be playing, you know, box fifth, you know, you know, uh, brandenburg's concerto in one group, and then you have, you know, Beethoven's fifth on the other, and at the same time it just doesn't work.

You know, two beautiful pieces of music, just not at the same time. So you wanna make sure that everything is working together in that concert. And that is where, you know, you have to think outside of your own myopic world.

And I don't mean that, I don't mean that pejoratively, I mean that, I mean that actually quite, quite admirably, right? We work so closely on the stuff that we do that it's often easy to forget that what we do influences other people. And if you don't actually work with, they're gonna go off and do their own thing, they're gonna create yet another standard.

They're gonna create another way of doing things, and then someone's gonna do something that's gonna take off, and then all of a sudden everybody's gonna flock over to that. So if you want your systems to work and you want your approaches to work over time, you've gotta rethink the significance and the influence of what you're working on in order to be able to make sure that not just you, but the people that rely on you are going to be able to accomplish this over time. It, that's a very valid point.

And I think it's interesting 'cause everything you're talking about can be to Steven's point, I can be right here in the core data center, and all those problems exist in one version of a world, and if I move out here to the edge, it all still exists. And then getting between the two becomes even more of a, a unique challenge. So it's, it's always kind of crazy to see that.

And your, your concept about the, the myopic and things like that reminds me of going way back into past to a tape-based storage, uh, war if, for those that are old enough to remember Betamax versus VHS, for example. That's a perfect example of kind of what we're trying to prevent here, both with Snia to Steven's point about the broadcast of evangelical side of things versus the, you know, nuts and bolts of bean myopic and building the really cool thing. But the really cool thing lost because they weren't playing well in the marketplace and, and the rest of the ecosystem didn't, uh, parlay along with them.

So, um, it's interesting to kind of always keep that in mind about, you know, past, present, future, and there's always going to be a, a fun new thing to work on. It's just where, and how far away from today's shiny object of ai, or how far physically it is edge versus data center in this whole of things. With that said, uh, Jay, uh, what are you most looking forward to, uh, what developments in the industry are you looking forward to?

Now that we have this monster, uh, business driver of ai, where are we going? I am most looking forward to, and this is more of a selfish me thing, I am looking more towards extrapolating the stuff that we are doing inside of these different things from a technical perspective and marrying them to the ethical and moral implications of accomplishing these goals. Alright?

I, I do think that we have a tendency as technical people to forget that what we do matters matters. And what I do, what I, what I do, what I do is because of the fact that in order to preserve those, those guidelines, you should have the most efficient, effective, accurate data when you need it, where you need it, so that you can make informed decisions. And so I think the infrastructure is not divorced from those questions.

It's not a software problem, it is an everything problem. And so I'm looking forward most to being able to have those kinds of conversations where the work that we do have a full understanding up and down the stack all the way to the end user so that people can feel more comfortable with using the technology without having to fear some sort of ai, you know, a GI moment of a, you know, of, of what, what's the word it's called? Um, the anoma.

The singularity. The singularity. There you go.

That's the word I was looking for. Um, and I, I honestly think, I, I honestly think that now is the time that we need to have those conversations, and I'm looking forward to having those. Yep.

And, and I'm gonna just go out on a limb and say, if you're interested in those, in those conversations, they're happening in the standards bodies. They're happening where people can get outside of their companies and out there talking to each other. Uh, again, I'll just put in a little plug, you know, my favorite tech, uh, event is, uh, is the SNA Storage Developer conference because it is so open and nerdy and fun and and engaging.

Uh, there are of course lots of other ones, you know, I mean, you mentioned OCP, which is always a lot of fun every single year. Super computing is always a lot of fun. Every year.

There's a lot of great conferences out there, and anytime you get people together to share ideas openly and to really explore what this all means, what you're gonna find is that people are not just technologists. one three version of the specification. They're interested in having the big picture conversations that Jay just described.

In fact, he and I have had big picture conversations with Scott, you know, sitting in the lobby of the hotel at, at these conferences. This is what it's all about. So I urge our listeners to get involved in these things.

Um, before we go, Jay, uh, tell us a little bit, how can people get involved in some of the, uh, standards bodies and events that you, that you go? org as a starting point. Um, you can contact myself or Scott.

I, you know, Scott is the chair of the Communication steering committee for SNA, and so he's responsible for communications. Um, can find me on LinkedIn, you know, with J Mets. Uh, I am wearing a hat, or actually, am I wearing a hat?

I think I'm wearing a hat inside of my picture there. I may not be aware. I know, but what the heck, what am I, what am I, what am I thinking?

org. And, um, and there's a lot of material there under the news, uh, situation. 0 specification.

We're gonna have a lot of material educating people on what that means, how it works, how to implement it. And so there's a lot of material in both organizations that are, that are coming down the road in the next six months or so that you can find out a great deal of, of, uh, information on these topics. How about yourself, Scott?

Uh, what's coming up for you? Yeah, so it was great to do the field day. We had the Snia events.

We have some, uh, future events coming up as well as planning for SDC. Uh, and if you're looking to kind of connect with myself, as Jay mentioned, you can find me through SN as well as I'm on, uh, former Twitter as SM Shaley and bluesky at SM Shaley as well, as well as LinkedIn. Feel free to, uh, drop a line and we'd be happy to talk.

Thank you very much. And as for me, you'll find me at s FoST on most social media networks, uh, in yes, including the X Twitter, as well as the Blue Sky and the Mastodon. And, uh, I would love to, to find y'all there.

Uh, you of course can find me as well on, uh, the, the Utilizing Tech podcast, the Tech Field Day podcast, uh, Textron Gang, uh, every Tuesday, and the, uh, uh, tech Field Day rundown, uh, some Wednesdays when I don't relinquish the chair to my friends, Tom and Al. So thank you very much for listening to this episode of Utilizing Tech. You can find this podcast in your favorite podcast applications.

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