On 8/18/2026 12:29 AM, Akihiko Odaki wrote:
On 2026/08/17 22:44, Huang, Honglei wrote:
On 8/17/2026 7:44 PM, Akihiko Odaki wrote:
On 2026/08/17 12:19, Huang, Honglei wrote:
Hi Michael, Alex, Dmitry, Akihiko,
Hi Honglei,
I'm bringing AMD GPU compute ROCm based on virtio. I posted a ROCm
over virtio
implementation to virglrenderer nine months ago (MR !1568 [1]). The
ROCm side has
been supportted by ROCm offical.
Current implementation is a virtio gpu context type capset handled
inside
virglrenderer, sharing the display path. That's an awkward fit, many
compute GPUs have no display engine at all.
I think "sharing the display path" conflates several layers and makes
the problem difficult to assess. It would help to identify the
concrete constraint behind "awkward fit."
End-to-end, there are four relevant layers:
1. Host GPU stack: hardware, host kernel, and host userspace
2. Paravirtualization stack: virglrenderer and QEMU
Yes we are asking can we add a new file like virtio-gpu specific for
compute, but maybe we can only add a new backend like virglrenderer
specific for compute.
3. Host/guest interface: virtio and the capset-specific command stream
In this plan we may need just add a capset id.
4. Guest GPU stack: guest kernel and guest userspace
Won't modify the guest kernel in this plan, this email list.
Orthogonally, acceleration is separate from display and scanout. A
physical device may provide both, but acceleration does not require a
display engine. Linux likewise exposes render and compute interfaces
separately from modesetting. The userspace interface virglrenderer
uses is messy; there is Vulkan, EGL, OpenGL, and now you are adding
ROCm. But there is one thing I must note is that acceleration and
display is decoupled, and acceleration does not require display.
Yes totally agreed.
At the protocol layer, context command buffers are carried by
VIRTIO_GPU_CMD_SUBMIT_3D. Scanout uses separate core virtio-gpu
commands, and VIRTIO_GPU_CMD_GET_DISPLAY_INFO may report no enabled
displays. At the implementation layer, QEMU handles scanout
presentation. virgl_cmd_set_scanout() obtains resource information
through virgl_renderer_resource_get_info() or
virgl_renderer_resource_get_info_ext(). That does not make scanout a
virglrenderer-owned display path.
Yes, agreed.
Therefore, if "sharing the display path" means sharing the same
device, control queue, and QEMU execution context, that identifies a
possible source of contention. If it means that capsets or
virglrenderer are inherently tied to display, I do not think that is
accurate. Vulkan compute is already used through Venus with libkrun
[2], and VCL proposes OpenCL support through virglrenderer [3].
Yes，but the vulkan is for GFX originally, and for some formal AI
frame work like pytorch, it's support is limited, and it performance
is lower than ROCm, and vulkan also lacks many AI infrastructure, like
composable kernel.
And for virCL, actually it is came from same project with ROCm native
context, but the original author didn't continue to support it, they
handed it over to someone else to take over. And in the first version of
virCL, it didn't pass the test of actual projects.
And it seems like virCL didn't upstream into virglrenderer also,
correct me if I am wrong.
I cited Venus and VCL only as examples showing that virtio-gpu and
virglrenderer are not intrinsically tied to display. I did not suggest
either as a substitute for ROCm.
Beyond that, sharing the display path is increasingly painful:
- Compute hammers the queues more than graphics, so sharing
virtio gpu's single control queue with display/virgl causes
contention
and display stutter.
All non-cursor commands do share one control queue, but a fence
avoids serialization.
yes, agreed.
There may still be implementation-level contention, and it is not
necessarily specific to compute. A sufficiently busy graphics workload
could expose the same bottlenecks. Possible contributors in current QEMU
include:
a) qemu_console_hw_gl_block() blocks the entire queue when QEMU only
needs to fence scanout commands.
Yes, agreed.
b) virtio_gpu_virgl_unmap_resource_blob() may also block the entire
queue just to delay one command.
Yes, but it is seems like it is must, someone else in AMD tried to use
async method to relase blob, but it failed to consistency issue, then
reverted to sync version.
Queue-wide suspension is not inherently required. Commit 4eb0aace85f5
("virtio-gpu: Support mapping hostmem blobs with map_fixed") added a
path that avoids per-blob MemoryRegion teardown when
virgl_renderer_resource_map_fixed() succeeds. The remaining path is also
being improved with:
https://lore.kernel.org/qemu-devel/20260424-force_rcu-v4-0-
[email protected]/
("[PATCH v4 0/6] virtio-gpu: Force RCU when unmapping blob")
Thanks. force_rcu is a clean fix for the RCU-reclamation part, but it
still keeps the unmap synchronous and serial.
c) QEMU dispatches the control queue and calls into virglrenderer from
its main-loop thread along with display work and many other things.
Venus's render server can offload renderer work, but control-queue
dispatch remains in QEMU's main loop.
Yes, we did some async optimization in ROCm context, but its
effectiveness is limited, see bellow.
In any case, I think you need to do some experiments to track down
the real cause. a) is easy to check: just comment out all
qemu_console_hw_gl_block() calls; it may corrupt display but removes
the blocking. b) can also be tested by leaking the mappings instead
of blocking the whole queue. Using a different display device like
qxl tells whether c) is causing contention.
Yes, totally agreed. following is my findings. In short words:
Optimization can reduce queue pressure, but it can't withstand
absolute overload because each command has some overhead. Making all
commands asynchronous would lead to a debugging hell about
asynchronous issues.
And we have high load applications rocmprofiler that continuously
catch information need virtio queue to handle. But create a new
backend can not solve it simply, we are trying to find a way. like
shmem between guest and host, then use cpu polling, bypass the virtqueue.
Most commands are fast on the CPU side, while heavy processing
happens asynchronously on the GPU. Cases (a) and (b) are exceptions.
The load is mostly memory management. Running an AI model allocates
and frees a large number of blobs. We already did some optimization
release them asynchronously, but the host processing is a single queue
one
process_cmdq, this is where the main bottleneck in my debugging work /
my understanding so far. I'm not certain it's the whole picture, so
please correct if I am wrong.
A model load or unload frees a large batch of BOs and allocates
another. Some of those commands are async in the virtio-gpu guest
driver, but QEMU still has to work through them on the one queue,
which takes time; so even though any single command is quick, there
are simply too many of them, the single queue backs up, and everything
behind it, gets delayed.
real work load (a few downstream customisations): loading one 16 GB
model (gemm4 e4b), drives ~1200 blob creates, a burst of
~1400 resource frees at teardown, ~3700 submits and ~6000 virtqueue
notifies, caused a 22 s guest soft lockup. And the behavior of memory
operations are controlled by upper layer like pytorch / HIP / runtime,
we can not control it.
To be honest, a separate backend won't fix this. But the real solution
maybe is compute specific. That logic is only useful to the compute
path, and folding it into the shared display device / renderer would
mean churning code that is mature and stable for graphics, with
regression risk. Keeping compute on its own instance and backend lets
us iterate on these compute only optimisations.
A 22-second lockup is too long for those command counts.
The most probable explanation I have is that the ROCm integration blocks
QEMU's main loop thread while synchronously waiting for GPU execution.
Creating separate devices won't resolve this because the main loop
thread is shared, and synchronously waiting on the GPU should be avoided
in the first place.
No synchronously waiting in ROCm backend, we are using user queue, and
event waiting, no sync operation in CMD wait. all the resource release
in ROCm are all async now.
Only the sync thing is memory thing mapping/unmapping in qemu, as long
as it remains synchronous, it will be overwhelmed by the massive number
of requests.
In any case, profiling is necessary before touching the implementation.
- Compute contexts need far more blob / shared memory than a
display one.
It is not a problem by itself. Frequent mapping and unmapping might
amplify the second issue above, but that needs to be measured.
Yes, agreed. I can give more detailed information.
- Maybe needs a wider ROCm / compute stack, cause the render
model fits poorly:
rocprofiler (PC sampling, SQTT/SPM, counters, high bandwidth
streams)
and ROCgdb (wave control, address watch, async exceptions an
out of band channel that must not block display).
virglrenderer does not impose a particular render model. That's why
Vulkan Compute just works with Venus.
Yes but vulkan is used for GFX initally. And can not support many AI
application.>
- Events, faults and GPU reset/SMI are async and don't map onto
fences.> - All of this is hard to extend cleanly inside a display
capset.
Capset is not about display but determines the protocol of the
VIRTIO_GPU_CMD_SUBMIT_3D command stream. You have described events,
faults and GPU reset/SMI are async don't map onto fences that may be
associated with VIRTIO_GPU_CMD_SUBMIT_3D which is dictated by capset.
An additional feature may be necessary, and it may or may not be
dictated by capset. The other things are irrelevant with the protocol
capset represents; they are either behavioral or about different
commands.
A fence is the one shot, but event is stateful and repeatable.
That may or may not be tied to capset. Agreed.
On the QEMU/host side, would something like this be OK? One step,
two parts:
- a dedicated headless virtio gpu instance for compute.
A second device would isolate its virtqueues and device-wide
renderer_blocked state. That may be useful if measurements show that
these are the bottlenecks, but it is not yet clear that they are or that
a second device is the appropriate solution.
- that instance served by a separate ROCm backend library loaded
in-process by QEMU.
First, I think we need to establish why ROCm cannot or should not remain
in virglrenderer. The virglrenderer, Venus, and VCL maintainers are
likely better placed to advise on that boundary. Once the protocol
requirements and performance measurements are clear, we can assess the
appropriate QEMU integration.
venus is borned for GFX.
virCL not merged.
To be clear, I'm not saying virglrenderer can't host a ROCm native
context it clearly can. My hesitation is more about fit and direction:
virglrenderer has grown up around GL/graphics, and I haven't yet found
compute oriented plumbing there to build on, while ROCm moves very
fast and I need something I can keep current with low friction.
Whether keeping ROCm in virglrenderer would create extra friction is
primarily a question for the virglrenderer maintainers. Its graphics
origins do not by themselves motivate adding a separate backend
interface to QEMU.
Fair. The first draft version in virglrenderer was in May 2024, and ROCm
has gone 5.7 → 7.14 in that window.
Regards,
Honglei
Regards,
Akihiko Odaki
Regards,
Honglei
[2] https://developers.redhat.com/articles/2025/06/05/how-we-
improved- ai-inference-macos-podman-containers
[3] https://www.qualcomm.com/developer/blog/2024/10/vcl-virtio-gpu-
opencl-driver
Regards,
Akihiko Odaki
That reuses the existing pluggable backend model, a second virtio
gpu + a
backend library. It doesn't add dedicated queues for debug/profiling
currently.
Waiting for reply and happy to share more detail. Thanks!
[1] https://gitlab.freedesktop.org/virgl/virglrenderer/-/
merge_requests/1568
Regards,
Honglei
