Alternate perspective: I use the heck out of session restore, and it has driven me nuts that it hasn’t worked properly under Wayland.
I tend to use different virtual desktops for different projects, so being able to reboot (because of a kernel update and needing to load a module or something) without losing and having to rebuild that state is is super valuable.
That’s credible.
I find the hardware architecture and licensing situation with AMD much more appealing than Nivida and really want to like their cards for compute, but they sure make it challenging to recommend.
I had to do a little dead reckoning with the list of supported targets to find one that did the right thing with the 12CU RDNA2 680M.
I’ve been meaning to put my findings on the internet since it might be useful to someone else, this is a good a place as any.
On a fresh Xubuntu 22.04.4 LTS install doing the official ROCm 6.1 setup instructions, using a Minisforum UM690S Ryzen 9 6900HX/64GB/1TB box as the target, and after setting the GPU Memory to 8GB in the EFI before boot so it doesn’t OOM.
For OpenMP projects, you’ll probably need to install libstdc++-12-dev in addition to the documented stuff because HIP won’t see the cmath libs otherwise (bug), then the <CMakeConfig.txt> mods for adapting a project with accelerator directives to that target are
find_package(hip REQUIRED)
list(APPEND CMAKE_PREFIX_PATH /opt/rocm-6.1.0)
set(CMAKE_CXX_COMPILER ${HIP_HIPCC_EXECUTABLE})
set(CMAKE_CXX_LINKER ${HIP_HIPCC_EXECUTABLE})
target_compile_options(yourtargetname PUBLIC "-lm;-fopenmp;-fopenmp-targets=amdgcn-amd-amdhsa;-Xopenmp-target=amdgcn-amd-amdhsa;-march=gfx1035"
And torch, because I was curious how that would go (after I watched the Docker based suggested method download 30GB of trash then fall over, and did the bare metal install instead) seems to work with
PYTORCH_TEST_WITH_ROCM=1 HSA_OVERRIDE_GFX_VERSION=10.3.0 python3 testtorch.py
which is the most confidence inspiring.
Also amdgpu_top is your friend for figuring out if you actually have something on the GPU compute pipes or if it’s just lying and running on the CPU.
Neat.
I set up some basic compute stuff with the ROCm stack on a 6900HX-based mini computer the other week (mostly to see if it was possible as there are some image processing workloads a colleague was hoping to accelerate on a similar host) and noticed that the docs occasionally pretend you could use GTT dynamicly allocated memory for compute tasks, but there was no evidence of it ever having worked for anyone.
That machine had flexible firmware and 64GB of RAM stuffed in it so I just shuffled the boot time allocation in the EFI to give 8GB to the GPU to make it work, but it’s not elegant.
It’s also pretty clumsy to actually make things run, lot of “set the magic environment variable because the tool chain will mis-detect the architecture of your unsupported card” and “Inject this wall of text into your CMake list to override libraries with our cooked versions” to make things work. Then it performs like an old GTX1060, which is on one hand impressive for an integrated part in a fairly low wattage machine, and on the other hand is competing with a low-mid range card from 2016.
Pretty on brand really, they’ve been fucking up their compute stack since before any other vendor was doing the GPGPU thing (abandoning CTM for Stream in like a year).
I think the OpenMP situation was the least jank of the ways I tried getting something to offload on an APU, but it was also one of the later attempts so maybe I was just getting used to it’s shit.
Don’t trust that they’re 100% compatible with mainline Linux, ChromeOS carries some weird patches and proprietary stuff up-stack.
I have a little Dell Chromebook 11 3189 that I did the Mr.Chromebox Coreboot + Linux thing on, a couple years ago I couldn’t get the (weird i2c) input devices to work right, that has since been fixed in upstream coreboot tables and/or Linux but (as of a couple months ago) still don’t play nice with smaller alternative OSes like NetBSD or a Haiku nightly.
The Audio situation is technically functional but still a little rough, the way the codec in bay/cherry trail devices is half chipset half external occasionally leads to the audio configuration crapping itself in ways that take some patience and/or expertise to deal with (Why do I suddenly have 20 inoperable sound cards in my pulse audio settings?).
This particular machine also does some goofy bullshit with 2 IMUs in the halves instead of a fold-back sensor, so the rotation/folding stuff via iio sensors is a little quirky.
But, they absolutely are fun, cheap hacker toys that are generally easy targets.
I see a lot of articles talking about the white elephants that might be lost from public view, which is probably the biggest tragedy, using their KI-10 as an example.
The one I’m most worried about from that collection is that they have the last known operational CDC6000 series machine (Theirs is a slightly smaller CDC6500, the flagship CDC6600 is the machine that made Seymour Cray famous, it fucked so hard it was 3x as fast as the previous title holder when came out in 1964 and was still the fastest machine in the world until 1969… when it was replaced by the derived, upgraded CDC7600 from 1969-1975).
It’s a 12,000lb, 80" tall, 165" on a side monster that draws 30kW (at 208V/400Hz), I haven’t heard a plan for it, and there are very, very few possible long-term-secure homes for such a thing.
I guess it’s just not in the current auction so it isn’t drawing as much attention yet?