GPU memory layout for custom type with different array lengths: StructArrays?

I have the following example of a structure for a data type (real type has more fields):

mutable struct Worker
    id :: Int   # name
    T  :: Int   # number of periods observed
    t  :: Int   # current period
    w  :: Vector{Float64}  # vector of wages in each period

where the data is such that T is potentially different for each Worker. The computational task involves evaluation of a likelihood function, and it is an operation that is conceptually similar to summing over w for each worker in an array W of Worker, and then summing the result for each worker:

julia> typeof(W)  

result = sum( sum( worker.w ) for worker in W )

Given that w is of different length for each worker, I cannot store the w data in a rectangular N,T = size(W) matrix, which would make it easier to parallelize the workload. In short, I have a list W and differently-long lasting computational tasks for each. Assume the list is long, like several million elements (Workers).

I would like to offload the computational task (sum(w)) to a GPU. I have been looking around and found the StructArrays package. From the last section of the readme I seem to gather that this could handle a non-standard datastructure like this one, but I’m not sure it’s the best solution. Any advice on this greatly appreciated!