Are AbstractArrays thread safe? I am trying to parallelize a code by using threads and am currently getting some performance gain by using Thread.@threads which shouldn’t come ideally if they were thread-safe.

```
Threads.@threads for i in 1 : length(x)
wndw_low = Int(max(1, low(i)))
wndw_high = Int(min(stencil_length, high(i)))
convolve!(x_temp, x, coeffs, i, mid, wndw_low, wndw_high)
end
```

```
function convolve!{T<:Real}(x_temp::AbstractVector{T}, x::AbstractVector{T}, coeffs::SVector,
i::Int, mid::Int, wndw_low::Int, wndw_high::Int)
#=
Here we are taking the weighted sum of a window of the input vector to calculate the derivative
at the middle point. This requires choosing the end points carefully which are being passed from above.
=#
@inbounds for idx in wndw_low:wndw_high
x_temp[i] += coeffs[idx] * x[i - (mid-idx)]
end
end
```

So my question is that whether the performance will improve if I let threads work on a separate array of their own or is this the optimal way to get performance?