Daniel,
I will have to get back to you later on the details of this because my
head is currently context switched to some infrastructure and
Kubernetes/golang work, so I am having a hard time digesting what you
are saying. I am new to the bpf stuff so this is about my own
learning as well as a conversation starter. The high level goal here
is to have a path for flexibility via a bpf program. Not just GPU or
DRM or CU mask, but devices making decisions via an operator-written
bpf-prog attached to a cgroup. More inline.
On Wed, Feb 3, 2021 at 6:09 AM Daniel Vetter <daniel@xxxxxxxx> wrote:
>
> On Mon, Feb 01, 2021 at 11:51:07AM -0500, Kenny Ho wrote:
> > On Mon, Feb 1, 2021 at 9:49 AM Daniel Vetter <daniel@xxxxxxxx> wrote:
> > > - there's been a pile of cgroups proposal to manage gpus at the drm
> > > subsystem level, some by Kenny, and frankly this at least looks a bit
> > > like a quick hack to sidestep the consensus process for that.
> > No Daniel, this is quick *draft* to get a conversation going. Bpf was
> > actually a path suggested by Tejun back in 2018 so I think you are
> > mischaracterizing this quite a bit.
> >
> > "2018-11-20 Kenny Ho:
> > To put the questions in more concrete terms, let say a user wants to
> > expose certain part of a gpu to a particular cgroup similar to the
> > way selective cpu cores are exposed to a cgroup via cpuset, how
> > should we go about enabling such functionality?
> >
> > 2018-11-20 Tejun Heo:
> > Do what the intel driver or bpf is doing? It's not difficult to hook
> > into cgroup for identification purposes."
>
> Yeah, but if you go full amd specific for this, you might as well have a
> specific BPF hook which is called in amdgpu/kfd and returns you the CU
> mask for a given cgroups (and figures that out however it pleases).
>
> Not a generic framework which lets you build pretty much any possible
> cgroups controller for anything else using BPF. Trying to filter anything
> at the generic ioctl just doesn't feel like a great idea that's long term
> maintainable. E.g. what happens if there's new uapi for command
> submission/context creation and now your bpf filter isn't catching all
> access anymore? If it's an explicit hook that explicitly computes the CU
> mask, then we can add more checks as needed. With ioctl that's impossible.
>
> Plus I'm also not sure whether that's really a good idea still, since if
> cloud companies have to built their own bespoke container stuff for every
> gpu vendor, that's quite a bad platform we're building. And "I'd like to
> make sure my gpu is used fairly among multiple tenents" really isn't a
> use-case that's specific to amd.
I don't understand what you are saying about containers here since
bpf-progs are not the same as container nor are they deployed from
inside a container (as far as I know, I am actually not sure how
bpf-cgroup works with higher level cloud orchestration since folks
like Docker just migrated to cgroup v2 very recently... I don't think
you can specify a bpf-prog to load as part of a k8s pod definition.)
That said, the bit I understand ("not sure whether that's really a
good idea....cloud companies have to built their own bespoke container
stuff for every gpu vendor...") is in fact the current status quo. If
you look into some of the popular ML/AI-oriented containers/apps, you
will likely see things are mostly hardcoded to CUDA. Since I work for
AMD, I wouldn't say that's a good thing but this is just the reality.
For Kubernetes at least (where my head is currently), the official
mechanisms are Device Plugins (I am the author for the one for AMD but
there are a few ones from Intel too, you can confirm with your
colleagues) and Node Feature/Labels. Kubernetes schedules
pod/container launched by users to the node/servers by the affinity of
the node resources/labels, and the resources/labels in the pod
specification created by the users.
> If this would be something very hw specific like cache assignment and
> quality of service stuff or things like that, then vendor specific imo
> makes sense. But for CU masks essentially we're cutting the compute
> resources up in some way, and I kinda expect everyone with a gpu who cares
> about isolating workloads with cgroups wants to do that.
Right, but isolating workloads is quality of service stuff and *how*
compute resources are cut up are vendor specific.
Anyway, as I said at the beginning of this reply, this is about
flexibility in support of the diversity of devices and architectures.
CU mask is simply a concrete example of hw diversity that a
bpf-program can encapsulate. I can see this framework (a custom
program making decisions in a specific cgroup and device context) use
for other things as well. It may even be useful within a vendor to
handle the diversity between SKUs.
Kenny
