> Mauro, I do not quite understand what you're saying about densely packed
> arrays, could you explain a bit more?
Consider:
julia> immutable A
x::Float64
y::Float64
end
julia> type B
x::Float64
y::Float64
end
julia> a = [A(4,5), A(5,6)]
2-element Array{A,1}:
A(4.0,5.0)
A(5.0,6.0)
julia> b = [B(4,5), B(5,6)]
2-element Array{B,1}:
B(4.0,5.0)
B(5.0,6.0)
Then in memory `a` is actually identical to:
[4., 5., 5., 6.]
(or
[4. 5.; 5. 6.] )
As Julia knows that the type of `a` is Vector{A}, it knows how to
interpret that junk of memory. Conversely, `b` is a vector of pointers
which point to the two instances of `B`. So, working with b is slower
than an Array{Float64,2} whereas a should be just as fast.
> Thanks,
> Chris
>
> On Tuesday, March 3, 2015 at 11:41:53 AM UTC-5, Mauro wrote:
>>
>> > I believe if all your type fields are concrete (which they are in the
>> case
>> > of Float64), the performance should be the same as using
>> Vector{Float64}.
>> > This is really nice since you get to use code that is much more
>> > understandable like state.x instead of state[1] for no penalty.
>>
>> I think to get densely packed array the type needs to be immutable:
>>
>> immutable StateVec
>> x::Float64
>> y::Float64
>> z::Float64
>> end
>>
>> Otherwise it will be an array of pointers.
>>
