How to run OpenCL file (example fast.cl) in OpenCV library?

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I want to run OpenCL file (example fast.cl at opencv310\sources\modules\features2d\src\opencl\fast.cl) in OpenCV library. But I see in this file have many kernels. And each kernel has a different number of arguments. How can I set up arguments to run this file?

You don't need to. You just run normal fast with UMat as the arguments.

it's mean when I change Mat by UMat. FAST function will be run on GPU. Do you know what fast.cl in OpenCV library use for?

I'm not entirely sure what you're asking. Here is where the fast.cl kernels are called from.

Again, you don't need to do anything but use UMat matrices and have compiled OpenCV with OpenCL support.

It means I only need to change from

Mat img = imread("chessboard.jpg", IMREAD_UNCHANGED);
FAST(gray, keypointsCorners, thresholdCorner, true);

to

cv::ocl::Device(context.device(0)); 
UMat img, gray; 
imread("chessboard.jpg", 1).copyTo(img); 
FAST(gray, keypointsCorners, thresholdCorner, true);

to have OpenCL code and this code will run on GPU? And while FAST () function is implementing it will call fast.cl file inside?

Try to use the code tag in the editor to make it readable.

Yes, so long as your OpenCV was compiled with OpenCL support, then it will run on the gpu. Though do note that in the snippet here, you don't actually fill gray. I assume you just left that out for brevity.

When I change Mat by UMat. My result is code ran on GPU. But it is slower than CPU. And when I change a name of "fast.cl" file in path: opencv310\sources\modules\features2d\src\opencl\fast.cl), Code still run normally. Whether fast.cl file was not called while the code is implementing. How do we know that when changing Mat by UMat, Code will run parallel on GPU?

Just to make sure, you are compiling the code? Not using the installer? The installer is not (I think) compiled with OpenCL, so you will see no benefit.

When I run code below

        cout << context.ndevices() << " GPU devices are detected." << endl;
    for (int i = 0; i < context.ndevices(); i++)
    {
        cv::ocl::Device device = context.device(i);
        cout << "name                 : " << device.name() << endl;
        cout << "available            : " << device.available() << endl;
        cout << "imageSupport         : " << device.imageSupport() << endl;
        cout << "OpenCL_C_Version     : " << device.OpenCL_C_Version() << endl;
        cout << endl;
    }

It showed results: 1 GPU devices are detected name: Quadro K2000 available: 1 image surport: 1

OpenCL version: OpenCL C 1.2

And while Code is implementing. It took 11026 ms for MAT and 29340ms for UMAT in the same time. Do you know why it is?

How many iterations? The first iteration may be much slower because of initializing the context and memory. That looks like a decent amount though.

Also, that's a really old card. What CPU do you have? If it's anything recent, it'll be faster than the GPU. Not because of the processing, but just the memory transfer. If you keep the same data on the GPU and do lots of work on it, it's much better than transferring back and forth.