Cyborg NVIDIA GPU Driver support vGPU management

The Cyborg NVIDIA GPU Driver has implemented pGPU management in the Train release, this spec proposes the specification of supporting vGPU management in the same driver.

Problem description

GPU devices can provide supercomputing capabilities, and can replace the CPU to provide users with more efficient computing power at a lower cost. GPU cloud servers have great value in the following application scenarios, including: video encoding and decoding, scientific research and artificial intelligence (deep learning, machine learning).

In the OpenStack ecosystem, users can now use Nova to pass gpu resources to guest by two methods:

  • Pass the GPU hardware to the guest (PCI pass-through).

  • Pass the Mediated Device(vGPU) to the guest.

With the long-term goal that Cyborg will manage heterogeneous accelerators including GPUs, Cyborg needs to support GPU management and integrate with Nova to provide users with gpu resources allocation in the aforementioned methods. The existing Cyborg GPU driver, NVIDIA GPU Driver, has supported the first method (PCI pass-through), while the second method is not yet supported. Please see ref 1 for Nova-Cyborg vGPU integration spec.

Use Cases

  • When the user is using Cyborg to manage GPU devices, he/she wants to boot up a VM with Nvidia GPU (pGPU or vGPU) attached in order to accelerate the video coding and decoding, Cyborg should be able to manage this kind of acceleration resources and to assign it to the VM(binding).

Proposed changes

To be clear, in the following, we will describe the whole process of how does the NVIDIA GPU Driver discover, generate Cyborg specific driver objects of the vGPU devices(comply with Cyborg Database Model), and report it to cyborg-db and Placement by cyborg-conductor. Features that are aleady supported in current branch is marked as DONE, new changes are marked as NEW CHANGES.

1. Collect raw info of GPU devices from compute node by “lspci” and grep nvidia related keyword.(DONE)

2. Parsing details from each record including vendor_id, product_id and pci_address.(DONE)

3. Generate Cyborg specific driver objects and resource provider modeling for the GPU device as well as its mdiated devices. Below is the objects to describe a vGPU devices which complies with the Cyborg database mode 4 and placement data model 5.(NEW CHANGE)

Hardware     Driver objects       Placement data model
   |               |                      |
1 GPU         1 device                    |
   |               |                      |
   |         1 deployable       ---> resource_provider
   |               |            ---> parent resource_provider: compute node
   |               |                      |
4 vGPUs     4 attach_handles    ---> inventories(total:4)

4. Supporting set the vGPU type for a specific GPU device in cyborg.conf. The implementation is similar to that in Nova 9.(NEW CHANGE)

  • Firstly, we propose [gpu]/enabled_vgpu_types to define which vgpu type Cyborg driver can use:

    enabled_vgpu_types = [str_vgpu_type_1, str_vgpu_type_2, ...]
  • And also, we propose that Cyborg driver will accept configuration sections that are related to the [gpu]/enabled_vgpu_types and specifies which exact pGPUs are related to the enabled vGPU types and will have a device_addresses option defined like this:

    List of physical PCI addresses to associate with a specific GPU type.
    The particular physical GPU device address needs to be mapped to the vendor
    vGPU type which that physical GPU is configured to accept. In order to
    provide this mapping, there will be a CONF section with a name
    corresponding to the following template: "vgpu_type_%(vgpu_type_name)s
    The vGPU type to associate with the PCI devices has to be the section name
    prefixed by ``vgpu_``. For example, for 'nvidia-11', you would declare
    Each vGPU type also has to be declared in ``[devices]/enabled_vgpu_types``.
    Related options:
    * ``[gpu]/enabled_vgpu_types``

    For example, it would be set in cyborg.conf

    enabled_vgpu_types = nvidia-223,nvidia-224
    device_addresses = 0000:af:00.0,0000:86:00.0
    device_addresses = 0000:87:00.0

5. Generate resource_class and traits for device, which later will also be reported to Placement, and used by nova-scheduler to filter appropriate accelerators.(NEW CHANGE)

  • resource class follows standard resources classes used by OpenStack 6. Pass-through GPU device will report ‘PGPU’ as its resource class, Virtualized GPU device will report ‘VGPU’ as its resource class.

  • traits follows the placement custom trait format 7. In the Cyborg driver, it will report two traits for vGPU accelerator using the format below:

    trait1: OWNER_CYBORG.

    trait2: CUSTOM_<VENDOR_NAME>_<PRODUCT_ID>_<Virtual_GPU_Type>.

    Meaning of each parameter is listed below.

    • OWNER_CYBORG: a new namespace in os-traits to remark that a device is reported by Cyborg when the inventory is reported to placement. It is used to distinguish GPU devices reported by Nova.

    • VENDOR_NAME: vendor name of the GPU device.

    • PRODUCT_ID: product ID of the GPU device.

    • Virtual_GPU_Type: this parameter is actually another format of the enabled_vgpu_types for a specific device set by admin in cyborg.conf. In order to generate this param, driver will first retrieve enabled_vgpu_type and then map it to Virtual_GPU_Type by the way showed below. The name is exactly the Virtual_GPU_Type that will be reported in traits. For more details about the valid Virtual GPU Types for supported GPUs, please refer to 8.

    # find mapping relation between Virtual_GPU_Type and enabled_vgpu_type.
    # The value in "name" file contains its corresponding Virtual_GPU_Type.
    cat /sys/class/mdev_bus/{device_address}/mdev_supported_types/{enabled_vgpu_type}/name
  • Here is a example to show the traits of a GPU device in the real world.

    • A Nvidia Tesla T4 device has been successfully installed on host, device address is 0000:af:00.0. In addition, the vendor’s vGPU driver software must be installed and configured on the host at the same time.

    [vtu@ubuntudbs ~]# lspci -nnn -D|grep 1eb8
    0000:af:00.0 3D controller [0302]: NVIDIA Corporation TU104GL [Tesla T4] [10de:1eb8] (rev a1)
    • Enable GPU types (Accelerator)

      1. Specify which specific GPU type(s) the instances would get from this specific device.

      Edit devices.enabled_vgpu_types and device_address in cyborg.conf:

      device_addresses = 0000:af:00.0
      1. Restart the cyborg-agent service.

    • Finally, traits reported for this device(RP) will be:



For the last parameter “T4_2B” (<Virtual_GPU_type>), we can validate the mapping relation between “nvidia-223” and “T4_2B” by check from the mdev sys path:

[vtu@ubuntudbs mdev_supported_types]$ pwd
[vtu@ubuntudbs mdev_supported_types]$ ls
nvidia-222  nvidia-225  nvidia-228  nvidia-231  nvidia-234  nvidia-320
nvidia-223  nvidia-226  nvidia-229  nvidia-232  nvidia-252  nvidia-321
nvidia-224  nvidia-227  nvidia-230  nvidia-233  nvidia-319
[vtu@ubuntudbs mdev_supported_types]$ cat nvidia-223/name

6. Generate controlpath_id, deployable, attach_handle, attribute for vGPU.(NEW CHANGE)

7. Create a mdev device in the sys by echo its UUID (actually is the attach_handle UUID) to the create file when vgpu is bind to a VM.(NEW CHANGE)

create_file_path= /sys/class/mdev_bus/{pci_address}/mdev_supported_types/{type-id}/create

8. Delete a mdev device from sys by echo “1” to the remove file when vgpu is unbind from a VM.(NEW CHANGE)

remove_file_path= /sys/class/mdev_bus/{pci_address}/mdev_supported_types/{type-id}/UUID/remove


Using Nova to manage vGPU device 10.

Data model impact


REST API impact


Security impact


Notifications impact


Other end user impact


Performance Impact


Other deployer impact

This feature is highly dependent on the version of libvirt and the physical devices present on the host.

For vGPU management, deployers need to make sure that the GPU device has been successfully virtualized. Otherwise, Cyborg will report it as a pGPU device.

Please see ref 2 and 3 for how to install the Virtual GPU Manager package to virtualize your GPU devices.

Developer impact




Primary assignee:


Work Items

  • Implement NVIDIA GPU Driver enhancement in Cyborg

  • Add related test cases.

  • Add test report to wiki and update the supported driver doc page




  • Unit tests will be added to test this driver.

Documentation Impact

Document Nvidia GPU driver in Cyborg project.