What does this OpenCL code mean? How does uchar16 work? (OpenCV laplacian3.cl source)

In https://github.com/opencv/opencv/blob/4.x/modules/imgproc/src/opencl/laplacian3.cl, line 107-118, I found the following code:

    arr[0] = (uchar16)(a, b.s0123, b.s456789ab, b.scde);
    arr[1] = b;
    arr[2] = (uchar16)(b.s123, b.s4567, b.s89abcdef, c);
    arr[3] = (uchar16)(d, e.s0123, e.s456789ab, e.scde);
    arr[4] = e;
    arr[5] = (uchar16)(e.s123, e.s4567, e.s89abcdef, f);
    arr[6] = (uchar16)(g, h.s0123, h.s456789ab, h.scde);
    arr[7] = h;
    arr[8] = (uchar16)(h.s123, h.s4567, h.s89abcdef, i);
    arr[9] = (uchar16)(j, k.s0123, k.s456789ab, k.scde);
    arr[10] = k;
    arr[11] = (uchar16)(k.s123, k.s4567, k.s89abcdef, l);

The variables a,b,c,d,e,f,g,h,i,j,k,l are declared as following:

    uchar a; uchar16 b; uchar c;
    uchar d; uchar16 e; uchar f;
    uchar g; uchar16 h; uchar i;
    uchar j; uchar16 k; uchar l;

I'm a little confused what does, for example, b.s0123 do? It doesn't look like normal C++ code to me. Maybe it's special OpenCL grammar?

In OpenCL, an uchar16 is a 16-element vector.

You can access its elements using a kind of "member" syntax. You can access individual elements (b.s0), or access several elements as another vector (b.s0123).

Given that b is an uchar16...

arr[0] = (uchar16)(a, b.s0123, b.s456789ab, b.scde);

That composes a new uchar16 (and assigns it to arr[0]), using the value of a and the first 15 elements of b (0 to e), accessed in three pieces: two 4-element and one 3-element vector. The values of b appear shifted to the right (the view is shifted left).

arr[1] = b;

That just copies the vector.

arr[2] = (uchar16)(b.s123, b.s4567, b.s89abcdef, c);

That's like the first case, except now the last 15 values of b are taken, along with c. This makes b appear shifted to the left now.

That pattern repeats another three times, with different variables.

Overall, that looks like a convolution is set up by preparing a (big) matrix. I'm guessing it's a 3x3 convolution (the shifts) and it can operate on 4-channel data (b,e,h,k).

That makes sense because the laplacian is calculated as a convolution of a kernel.

You are not gonna learn much about the laplacian or convolutions from those implementations. These implementations are code. Code does not explain the theory.