About GPU

Zivid SDK를 최적으로 실행하려면 호스트 머신에 GPU가 필요합니다. 내장 GPU와 전용 GPU 모두 Zivid와 호환되며, 성능 차이는 캡처 시간에 따라 달라집니다.

Performance Considerations

Dedicated GPU

A dedicated GPU gives the best performance with Zivid. 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:

데스크탑용 NVIDIA GeForce GTX 1060 이상
노트북용 NVIDIA GeForce MX150 이상

Integrated GPU

This is the most cost-efficient solution for Zivid. 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:

HD630 이상의 Intel i7

Nvidia Jetson Orin

Zivid는 Zivid SDK와 추가 소프트웨어를 실행하기 위해 최소 16GB RAM이 있는 Jetson Orin 보드를 권장합니다.

Zivid SDK는 Jetson Orin Nano를 지원하지만, Orin Nano는 CPU와 GPU가 공유하는 총 RAM 용량이 4GB/8GB에 불과합니다. 4GB는 일반적으로 Zivid SDK를 사용하는 애플리케이션을 실행하기에 부족하며, 실제로 8GB는 많은 사용 사례, 특히 장시간 캡처, Stripe/Omni 엔진 사용, 다양한 필터 활성화 등에 부족합니다. 따라서 Orin Nano를 사용할 때는 애플리케이션의 RAM 사용량을 신중하게 평가해야 합니다.

경고

Jetson Orin boards may boot in a reduced power profile that significantly slows down Zivid SDK point cloud processing. Set the maximum power profile as described in Install Zivid on Jetson Linux.

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. You can also configure Zivid SDK to run on a CPU, but CPU is an unsupported compute device mode.

CUDA and OpenCL

Zivid SDK is available in two versions, a CUDA version and an OpenCL version. The version is selected when you install the SDK, not in the configuration file.

The CUDA version runs on NVIDIA GPUs only. The OpenCL version runs on Intel GPUs, and can run on NVIDIA GPUs as an unsupported compute device. To use an NVIDIA GPU as a supported compute device, install the CUDA version of the SDK.

Configuration file

해당 파일이 아직 없으면 Config.yml 파일을 다운로드하여 다음 디렉토리에 넣으세요.

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

단일 공급업체의 GPU가 여러 개 있는 경우(예: Nvidia GPU 두 개), SelectionFilter 사용할 GPU를 지정해야 합니다. clinfo 사용하여 모든 컴퓨팅 장치를 나열합니다. 아래 드롭다운을 따라 clinfo 에 대한 자세한 내용, 설치 방법 및 사용 방법을 확인하세요.

특정 GPU 이름을 찾으려면 clinfo 실행하여 컴퓨팅 장치를 나열하세요.

clinfo -l

참고

clinfo lists OpenCL devices and applies to the OpenCL version of the SDK. With the CUDA version, run nvidia-smi -L in a terminal instead, and use any unique part of the CUDA device name. Both commands run on Windows and Ubuntu. On Windows, clinfo is not installed by default and nvidia-smi may not be on the PATH.

구성 파일의 SelectionFilter 원하는 장치 이름의 고유한 부분으로 변경하세요. 예를 들어, 다음 출력에서 NVIDIA GeForce RTX 3060의 경우

../../../_images/clinfo_multiple_gpu_single_vendor.png

SelectionFilter 다음과 같이 설정합니다.

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, AMD GPUs, and NVIDIA GPUs on the OpenCL version of the SDK.

해당 파일이 아직 없으면 Config.yml 파일을 다운로드하여 다음 디렉토리에 넣으세요.

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/

구성 파일에는 다음 정보가 포함되어 있습니다. 여기서 AllowUnsupported: yes 지원되지 않는 컴퓨팅 장치를 활성화합니다.

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

Enable NVIDIA on the OpenCL version

The OpenCL version of the SDK treats NVIDIA GPUs as unsupported compute devices. To run the OpenCL version on an NVIDIA GPU, set Vendor to Nvidia and AllowUnsupported to yes in the Config.yml. For a supported setup on NVIDIA GPUs, install the CUDA version of the SDK instead.

__version__:
    serializer: 1
    data: 19
Configuration:
    ComputeDevice:
        Type: GPU
        Vendor: Nvidia
        AllowUnsupported: yes

Enable CPU

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, and Vendor to Intel in the Config.yml.

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

경고

CPU is an unsupported compute device mode. Capture time will be greatly increased when using CPU and this setup is not recommended for most applications. In mixed OpenCL platform setups, CPU selection can also cause unstable behavior, including crashes.

Version History

SDK	Changes
2.18.0	Added CUDA version of the SDK for NVIDIA GPUs. Added Jetson Thor support. Added support for Ubuntu 26.04.
2.12.0	Jetson Orin 지원이 추가되었습니다.