About GPU

Zivid SDK requires a GPU on the host machine to run optimally. Both integrated and dedicated GPUs can work well with Zivid, where the difference in performance lies in the capture time you get.

Performance Considerations

Dedicated GPU

It is also the best choice if the GPU will be used for more than Zivid’s computations. A medium to high-end NVIDIA GPU with at least 3 GB of memory is required for optimal performance. This is the preferred solution for Zivid.

Recommendations:

NVIDIA GeForce GTX 1060 or better for desktops
NVIDIA GeForce MX150 or better for laptops

Integrated GPU

The benefit of an integrated GPU is that the data transfer from GPU to CPU is fast. It is also beneficial for applications that require low power consumption, e.g., battery-powered mobile platforms. A high-end integrated GPU with at least 3 GB of memory available is required for optimal performance.

Recommendations:

Intel i7 with HD630 or better

Nvidia Jetson Orin

Zivid recommends Jetson Orin boards that have at least 16GB of RAM for running Zivid SDK and additional software.

While Zivid SDK supports Jetson Orin Nano, Orin Nano has only 4GB/8GB of total RAM, which is shared between the CPU and the GPU. 4GB is generally insufficient to run any application using Zivid SDK, while 8GB in practice is insufficient for many use cases, especially for doing longer captures, using Stripe/Omni engine, and/or enabling many filters. Therefore, when using Orin Nano, RAM usage in your application must be carefully evaluated.

Selecting Compute Device

If you have multiple GPUs and want Zivid SDK to use a specific one, you can specify the compute device in the Configuration file. This is also the case if you want to use Zivid SDK with a CPU.

If the file does not already exist, download the Config.yml file and place it in the following directory:

Windows

mkdir %LOCALAPPDATA%\Zivid\API
move %HOMEPATH%\Downloads\Config.yml %LOCALAPPDATA%\Zivid\API\

Ubuntu

mkdir --parents "${XDG_CONFIG_HOME-$HOME/.config}"/Zivid/API
mv ~/Downloads/Config.yml "${XDG_CONFIG_HOME-$HOME/.config}"/Zivid/API/

The desired GPU is assigned by changing the ComputeDevice parameters in the configuration file.

Single vendor

If you have multiple GPUs from a single vendor (e.g. two Nvidia GPUs), SelectionFilter must specify which GPU to use. We will use clinfo to list all compute devices. Follow the dropdown below for more information about clinfo, how to install it and how to use it.

To find the specific GPU name, run clinfo to list your compute devices.

clinfo -l

Change the SelectionFilter in the configuration file to be any unique part of the name of the desired device. For example, for the NVIDIA GeForce RTX 3060 in the following output

../../_images/clinfo_multiple_gpu_single_vendor.png

you would set SelectionFilter to

ComputeDevice:
    SelectionFilter: "RTX 3060"
    Type: Any
    Vendor: Any

Multiple vendors

If you have multiple GPUs from multiple vendors (e.g. an integrated Intel GPU and a dedicated NVIDIA GPU), change Type to GPU and Vendor to NVIDIA or Intel, e.g.

ComputeDevice:
    SelectionFilter: ""
    Type: GPU
    Vendor: NVIDIA

Enabling unsupported compute devices

Enabling unsupported compute devices can be done by enabling an option in the Configuration file. However, such a setup may give unexpected results and errors, and is not tested by Zivid. Currently, the unsupported compute devices are CPUs and AMD GPUs.

If the file does not already exist, download the Config.yml file and place it in the following directory:

Windows

mkdir %LOCALAPPDATA%\Zivid\API
move %HOMEPATH%\Downloads\Config.yml %LOCALAPPDATA%\Zivid\API\

Ubuntu

mkdir --parents "${XDG_CONFIG_HOME-$HOME/.config}"/Zivid/API
mv ~/Downloads/Config.yml "${XDG_CONFIG_HOME-$HOME/.config}"/Zivid/API/

The config file contains the following information, where AllowUnsupported: yes enables unsupported compute devices:

__version__:
    serializer: 1
    data: 19
Configuration:
    ComputeDevice:
        AllowUnsupported: yes

Enable CPU

경고

Capture time will be greatly increased when using CPU and is not a recommended mode for most applications.

It is possible to run the Zivid SDK on an Intel chipset using their CPU Runtime for OpenCL. Modify the compute device Type to be CPU in the Config.yml.

__version__:
    serializer: 1
    data: 19
Configuration:
    ComputeDevice:
        Type: CPU
        AllowUnsupported: yes

Version History

SDK	Changes
2.12.0	Added Jetson Orin support.

About GPU

GPU Requirements

Performance Considerations

Selecting Compute Device

Enabling unsupported compute devices

Version History