What is `Base.allocatedinline` and custom types for `CuArray` (`CUDA.jl`) involving `Missing`

Specific Domains GPU
,
2

There’s several issues with this MWE. First of all, cpu_arr is a type, not a value. Your MyStruct should be parametric because Vector can never live on the GPU. Then there’s the problem of arrays-of-arrays, which doesn’t really work, see Arrays of arrays and arrays of structures in CUDA kernels cause random errors - #12 by fedoroff.

An example how this could work:

using CUDA, Adapt

struct MyStruct{T <: AbstractVector{Tuple{Int,Int}}}
    arr::T
end
MyStruct(x::T) where T = MyStruct{T}(x)
Adapt.adapt_structure(to, x::MyStruct) = MyStruct(adapt(to, x.arr))

function main()
    cpu_arr = [MyStruct(Vector{Tuple{Int, Int}}())]

    # first, convert the contained MyStruct{Vector} elements
    gpu_arr = adapt.(CuArray, cpu_arr)

    # we could now convert gpu_arr::Vector to a CuArray, but since we'll also have to
    # convert the MyStruct{CuArray} elements to MyStruct{CuDeviceArray} (because @cuda
    # doesn't automatically convert array _elements_ when calling a kernel), we'll
    # leave it on the CPU

    GC.@preserve gpu_arr begin
        gpu_device_arr = cudaconvert.(gpu_arr)
        final_gpu_arr = CuArray(gpu_device_arr)

        # final_gpu_arr is now a CuArray of MyStruct{CuDeviceArray} elements, which we can
        # pass to a kernel (@cuda will do the outer CuArray->CuDeviceArray conversion)
        @show typeof(final_gpu_arr)
    end
end

But I recommend simplifying the setup instead.