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There are cases where this behaviour is even the > > > consensus among vendors, as they collectively prefer keeping the design > > > effort low and get drivers and whole new subsystems upstream without > > > taking the community interests into account at all. > > > > I've begun to have the opinion that the old incentive structure in the > > industry has fallen apart. > (...) > > Now we have single entities that are OEM/(Largest) Customer/OS vendor > > and sometimes even Chip designer all rolled into one opaque box. They > > run, and only care about, a completely proprietary stack in userspace. > > I have a more optimistic view. > > Maybe it depends where you look. Yes, I didn't say, but I'm specifically looking at the Datacenter/Cloud/Enterprise area - not Android. Arguably Android is more classical with Google acting principally as an OS Vendor. > For deeply embedded silicon even in datacenters, companies like > Red Hat have pushed the vendors to work upstream because they > don't want to carry any custom patches. Jon Masters has been > instrumental here, requiring upstream drivers and ACPI support for > server silicon. The influence of the OS vendor in this space has declined considerably. No hyperscale cloud uses Red Hat as the hypervisor OS. Many now even provide their own in-house preferred OS for the VMs to use. This is what I mean, take Google Cloud as example. Their cloud side has a propritary closed hypervisor environment. They are their own OEM, manufacturing their own systems. They have their own hypervisor OS and VM OS that they control. They even make some of their own chips, and have vendors make customized off-the-shelf chips just for them. IMHO there are a number of surprising and new motivations that come from this consolidation - this is not the familiar dynamic. It pus Linux in the role of de-facto standards body without the tools of a standards body to manage the situation. > For drivers/accel I was under the impression that since LF is backing > PyTorch that would be the default userspace, but I don't know how they > stand with that as it seems CUDA-centric for accelerators, and > admittedly I don't know what conformance would mean in that case. > What is even the backend API for an accelerator userspace? > CUDA and OpenCL? Yeah, there is a big glue layer between pytorch and the actual HW. I feel the industry settled on things like pytorch as the agreed interop layer, and left alone, the driver layer below. So we have CUDA/ROCm/OneAPI as all different HW stacks leading up to pytorch. This is not even new, in HPC networking for the least 30 years we've had MPI as the agreed interop layer and under MPI are fullly parallel per-device stacks - with varying levels of openness. At least classical HPC had several well funded actors who had a strong incentive to follow open source methodologies. AI HPC hasn't reached an incentive yet, it is too new and exciting. > For GPIO (admittedly a smaller problem than GPU) we simply made > a new uAPI to supercede the old one when it didn't work out. To use Dan's example, if we have to call a mullagan on Confidential Compute uAPIs it would be a total disaster as we would be unable to remove the old ones, ever. Some cloud operator would build their proprietary software stack on the old API and simply refuse to ever change. This stuff is huge and super complicated, so the cost of keeping two would be impractical. I think your point of try-and-fail-fast only works if we have the flexability to wipe out the failure. If the failure lasts forever the cost of failing can become too great. Jason