Hello,

I’m trying to write a kernel that does some operations on vectors using CUDA.jl and @cuda. My code is essentially this:

```
points = Nx3 matrix with x,y,z coordinates of points
elements = Nx4 matrix with integers that list rows in "points" making up quadrilaterals
A = NxN matrix
for i in 1:N
for j in 1:N
a bunch of operations (and, unfortunately, some if statements) involving the points in elements[i,:] and elements[j,:]
A[i,j] = a function of those operations
end
end
```

A lot of the operations involve `dot`

and `cross`

. On the CPU I deal with that in a pretty clean way. Say:

```
point1=points[elements[i,1],:] # gives me an array with 3 numbers x,y,z
...
vec1 = point1-point2;
...
foo = dot(vec1,vec2)
bar = cross(vec1,vec2)
```

Is there an elegant way to do that on the GPU? Trying to use `[i,:]`

doesn’t seem to work and extracting subvectors of an array doesn’t seem too easy (also, assigning subvectors of an array! Say A[1,:] = b).

I don’t even know how to create an array in a kernel. The only way that I managed to do some math was to index each individual position and do the calculations in the classic way (i.e., old school C-style, not Matlab-style).

Thanks a lot!