WEBVTT

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All right, can I at least some people hear me?

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Yeah, all right.

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I hope you're all here for the next talk, right?

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You're not here for this one.

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I hope okay, anyway.

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So I'm JPE, I work at AMD.

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If that wasn't obvious, I put it there as well.

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First off, I wanted to say that I'm also the

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Organized of the LVME TARP in Domestead,

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or the LVM Social.

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Like those were mentioned.

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So if you are around the Domestead area in Germany,

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you know, we have a meet-up every last Wednesday of every odd month.

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So the next one will be in March.

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So if we're free to join us, we don't know yet, you know,

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what will be the topic, but anyway.

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All right, so I'm going to talk about LVM offload.

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Anybody heard about LVM offload so far?

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Raise your hand. Oh well, okay.

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That's more people than I expected.

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Cool, that's great.

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First off, very important disclaimer.

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Most of what I'm going to talk about is not something

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that I actually work on.

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So the credit goes to all the people who actually

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like implement the stuff.

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Mentioning mostly AMD codeplay and intel,

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and there's also LNNL working on this,

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at least that's what I saw from the PRs,

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and those are the people who joined the community meetings that we have,

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which are every two weeks,

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and they alternate between the OpenMP,

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which is mostly OpenMP offloading in LVM,

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and this like general LVM offload meeting.

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Okay, so let me start off with some sort of a vision of the LVM offload.

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By the way, are you showing me minutes for the full slot?

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Okay, awesome.

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So the vision of this is that we,

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in accordance to kind of the LVM idea that we provide

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a composable library, or a set of composable libraries,

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that allows you to build GPU offloading libraries on top of it.

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Right, so let's say you have some language

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that you want to support GPU offloading in your language,

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and so you can build on top of these LVM offload libraries

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to actually make that happen.

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So you don't need to worry about a lot of these things.

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And the other important thing is, of course,

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it should be cross vendor, right?

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It should not just be AMD or intel or in video whoever, right?

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It should probably also be not necessarily GPU specific,

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but right now it is GPU specific.

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And the green should be blue, but that's just anyway.

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So history.

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I duck through a bit of the history of the LVM offload project

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to see, you know, how's it going?

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And a lot of the credit here actually goes to your Honest Erfor

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who started this with an RFC on this course on the 22nd October

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in 2023.

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And then we had a lot of discussions around how it should be done.

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So everybody agreed, hey, this is a great idea.

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We should go ahead and do this, right?

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We had Lip on Target, which is the OpenMP offloading library implementation.

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That was entry for a long time,

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and we wanted to move it into this top level offload idea.

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How should we go about this?

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Should we just create a new one from scratch?

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You know, do everything from scratch, and then at some point

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re-implement Lip on Target sitting on top of it,

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or should we move Lip on Target into offload?

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And then, you know, make it better, make it so that it actually is

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LVM offload.

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I'm a fan of starting from scratch with the offload library,

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and let's say we did it the other way.

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You know, sometimes you win, sometimes you lose, I guess.

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But so we decided that this is the way to go,

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and so on the third April, 2024,

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the PR got landed that actually put this offload top level

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of the directory in three.

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And two days later, there was a heads-up post on this course

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that this is going to happen, or just kind of an interesting

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time on here, but anyway.

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And then, again, a little bit later on the 22nd of April,

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the PR landed that moved Lip on Target into Lip offload,

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or into offload into the directory.

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In between, we did a lot of work upstream and downstream,

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so our rock is a fork of the rock and compiles

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as a fork of LVM, and we needed to figure out some of the

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mechanics about this move because we have downstream stuff

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that's downstream for several reasons,

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but that's a different discussion.

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So we finally worked with the community to get this done

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and without breaking everything, so that's great.

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And then, that's still Lip on Target, right?

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And then, basically, we did write the offload API

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from scratch why we also moved Lip on Target.

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So we're kind of doing the thing that we decided to not do,

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but we did it nevertheless.

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So that was kind of interesting.

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And then on 25th November, 2024,

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APR landed that introduced the first offloading API,

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and I see we get some, we get lighted here.

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Thank you so much.

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I see the light.

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Anybody here loves the movie Boost Brothers?

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No, I'm the okay, I don't know where.

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Anyway, so we now have the offload API.

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And then, quite recently, on the 15th December last year,

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Intel landed the PR that actually adds support for Intel GPUs

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into offload, which is great, so you can now offload

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onto Intel GPUs without stream trunk.

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So what is the offload?

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So let's look a little bit of inside offload.

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So you have some sort of user application at the top,

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or maybe an offload library that you're writing yourself,

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and you have some accelerators down there.

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And then there's the good ol' Lip on Target

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that everybody loves for the flakiness in the testing, right?

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And so, we have all of this infrastructure.

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And then, as I said, right next to it,

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there is the Lip offload, which has an API that we're still developing

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and it has a bunch of unit tests.

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And probably one of the more interesting things is that

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beneath both of them, we have what is called the plugins.

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And I think for some legacy reasons,

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they're called plugins next gen, because we already had some plugins

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and everybody replaced them with some newer ones.

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And the plugins are what basically abstracts away

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the vendor specific details, right?

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So there's one for AMD GPU, there's one for Nvidia,

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and now there's also one for Intel's level zero runtime.

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And so the plugins expose a C++ API

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that then lip offload sits on top of.

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And that design is by choice because at some point,

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we actually want to have lip offload to be

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a stable vendor, agnostic, and generated API.

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So we use table gen for the API, the APIs, you know,

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everybody loves table gen, right?

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Isn't table gen, so it's generated,

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and then it at some point calls into these plugins.

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One of the interesting parts with

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we're generating the actual offload API,

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is that we also generate a bunch of argument,

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checking, and log capabilities with it.

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So you can basically, like for free,

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you get complete tracing of the API,

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you get complete argument validation of the API.

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And so that is why we wanted to settle for table gen,

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and then kind of split this definition of the API

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and table gen, and having the actual implementation done by hand.

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So if you want to add something to the offload API,

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the workflow you follow is basically,

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you add an API entry point into the offload table gen files,

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you rebuild the target, so you would generate a bunch of things.

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And then you copy paste, generate the declaration

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into the offload impulsy pp,

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and you go ahead and implement it in terms of the plugins,

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and then you actually rebuild the whole thing again,

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you basically now have a functioning API.

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So that's kind of the idea there.

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And so right now, this is working,

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and it's unetested, so pretty much the whole API is unetested.

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That's already running in the build box that we have.

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And once we have more settled onto,

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like how this is actually going to work,

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we're going to put together some smoke tests and integration tests,

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and whatever you want to have, right?

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So that we make sure that it's actually working.

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And now to some, so that's offload,

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and this is great, right?

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And then, well, sometimes upstream versus downstream

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is still a bit painful,

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and we experience this,

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because as I said,

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we have downstream changes in the bump target

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for OpenMP offloading,

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which is the OMpt or the OpenMP Profiler,

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the OpenMP Tools interface, sorry.

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Now, offload and the bump target use the plugins, right?

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So kind of they make use of this thing.

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The problem is that downstream,

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some of the OMpt functionality is actually

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intrusive into the plugins.

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So you change the plugins,

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you refer to symbols that aren't there,

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and then when you actually try to build either of those,

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you either get undefined reference errors,

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or multiply defined reference errors.

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So that's a bad position to be in.

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Now, as I said,

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the basically comes from the situation that inside the plugins,

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you're using OMpt symbols,

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and for historic reasons those are defined,

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or maybe for reasons that make sense,

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those are defined inside the bump target,

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because the bump target is again,

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that's the OpenMP offloading library,

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and so OMpt is OpenMP specifics

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or goes into the bump target.

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So, what did we do to fix this?

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We didn't want to touch the bump load, of course, right?

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What we did is,

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you basically introduced a new

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generic profiler into the plugins,

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or into the whole ecosystem,

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with the ideas that all this profiling is now implemented

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in terms of this generic profiler interface.

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So the plugins only call into the generic profiler,

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and then the generic profiler,

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or at the implementation 40MPT

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inherits from the generic profiler,

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refers to all the OMpt symbols,

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and they are still defined inside the bump target.

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So you kind of split this.

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And the way that this works is that inside the plugins,

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you have only a reference or a pointer

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to a specific profiler that you want to use at runtime,

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and so lip-on-plug it instantiates

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that with an OMpt profiler,

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whereas if you're going to use a lip-offload,

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it simply uses the implementation of the generic profiler

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that's there already, right?

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With that approach,

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you can also think of having some sort of basic profiler

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that would allow you to just generate CSV files,

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or your kernel timings,

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or for some API functions or whatever.

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So it gives you this abstraction away

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from being specific to one program language.

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Now, we still have OMpt symbols,

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kind of used,

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referred to and defined in lip-on-target,

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and in this profiler stuff.

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So eventually what we want to have,

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we actually want to encapsulate that into a lip-on-t,

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which gives you all this OMpt functionality

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that you can then use for the lip-on-target,

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and for the generic profiler,

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so when you build the open and piece of,

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you're making use of lip-on-t.

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And so to really package these things

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more into specific use case libraries,

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then follow this general idea of having

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the separate libraries,

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the separate interfaces,

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these composable things that you want to have,

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and so you can think of even for the buffalo

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providing other implementations

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of the generic profiler that you can link into,

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whatever runtime you want to build

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when you're constructing this runtime.

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And so that's it,

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actually for my talk.

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I have a bunch of references here.

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I have to show you the slide.

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And with that, I'm very happy to ask,

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to answer questions, of course.

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So we have five minutes left for questions.

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Okay.

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Yes.

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Can you speak up a little bit more?

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What's the forecast for getting apples and apples?

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What's the forecast for getting apple support?

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I guess if someone wants to open a PR,

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you can do that.

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No, I don't know.

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I don't know if anybody is working on apple support.

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I don't know of anybody.

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But yeah, you're absolutely,

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if someone has the ability to do that,

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I think we would be super happy to support it.

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Yeah.

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Yes.

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Yes.

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So, are you talking about India, India,

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etc?

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Where would India,

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lucky stuff inside this open source code?

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Okay. So, I talked about AMD and Nvidia.

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And so, where would Nvidia plug in here?

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So, what we actually do these plug-ins,

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as I said, so we have one for Nvidia,

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you can offload onto Nvidia GPUs with upstream right now.

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Basically, the plug-in is implemented in terms of CUDA.

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So, we just, you know,

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your regular CUDA API, so you CUDA malloc,

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CUDA launch, CUDA whatever, right?

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So, that works.

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Yes.

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Yes.

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This is also used by Rock Prof,

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AMD's Rock Prof V3.

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You mean the OMPT stuff?

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No.

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I think Rock Prof V3 uses their own mechanism

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of the way on how they thank you,

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their kernel timing mechanisms.

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But we can discuss this more offline.

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I don't think that's relevant for this audience here.

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More questions?

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Yes.

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Okay. So, what happens if we have multiple GPUs in your system?

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Which one gets picked up?

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So, for lib offload,

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the API exposes basically an iterator

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through the different devices that you have.

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And so, it would be up to a runtime sitting on top of it

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to define semantics on what happens.

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For OpenMP, the semantics is that you always pick the first device

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unless specified otherwise by a user.

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Right? So, in OpenMP, you can say,

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I want to use device 3,

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and then it will pick up device 3.

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If you don't say anything,

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it will pick up the default device,

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which would be default device 0.

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Yes.

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How likely is it to combine multiple GPUs from different vendors?

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Different vendors as well?

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That would be great.

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Okay.

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So, I don't know for sure,

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but I believe you can actually do that today already.

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I only know, so the OpenMP lib on target,

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you have ways to specify which accelerator you want to use in OpenMP,

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and there you can specify I want to use an AMD accelerator

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or an Nvidia accelerator,

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and then it will match whatever accelerator you have.

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So, it should work, you know, TM.

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But if it doesn't,

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you should open an issue on upstream MLVM,

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because it's supposed to work.

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Love question.

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Okay, that's question.

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Yes.

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How explicit is the handling of data transfers?

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So, in lib off load,

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you would be required to do everything manually.

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In OpenMP, it's kind of implicit,

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because runtime does it for you.

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But inside the runtime,

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it's again explicit, of course.

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In OpenMP, you would say,

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Pregnant target teams, blah, blah, blah, blah, blah,

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so it's parallel on the GPU.

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And then when you hit that kernel,

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the runtime will automatically generate

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the data transfers for you,

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and we'll put that onto the device.

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Run the kernel and then pull the data back.

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If you were to use the Offload API,

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you would have explicit calls to do that.

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Right?

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So, you would say, as well,

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copy host device, launch kernel,

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and device vector host.

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Because again, so offload is meant as a foundational layer

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on top of which you build your actual runtime.

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So I don't know, like, cray or HP, HP, X or whatever.

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Cool, thank you.

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Thank you.