Your benchmark is flawed.
Try something like the Benchmarks
<https://github.com/johnmyleswhite/Benchmarks.jl> package, or use this:
using FixedSizeArrays
Base.(:+){T}(p1::NTuple{2,T}, p2::NTuple{2,T}) = (p1[1]+p2[1], p1[2]+p2[2])
function test(a, b, N)
ct = a+b
for _ in 1:N ct = ct+a+b end # use intermediate result so that llvm
doesn't just remove the loop
ct # return value so it doesn't get optimized away
end
a, b = randn(2), randn(2)
at, bt = (randn(2)...), (randn(2)...)
p1, p2 = Point2D(randn(2)...), Point2D(randn(2)...)
#warm up
test(a,b,10^7)
test(at,bt,10^7)
test(p1,p1,10^7)
@time test(a,b,10^7) # 1.175718 seconds (40.00 M allocations: 1.788 GB,
9.77% gc time)
@time test(at,bt,10^7) #0.017781 seconds (6 allocations: 208 bytes)
@time test(p1,p1,10^7) #0.017783 seconds (6 allocations: 208 bytes)
As you can see, tuples and types are pretty much the same, while Vector is
far behind.
In this benchmark, it's mainly because the Vector is heap allocated.
Tuples are preferable to types, since they have the same performance, but
you can implement n-dimensional point types.
If you already found FixedSizeArrays, what's holding you back of using it?
It already has the Point{N, T} type and GeometryTypes.jl
<https://github.com/JuliaGeometry/GeometryTypes.jl> has even more ;)
Best,
Simon
Am Montag, 11. April 2016 22:00:59 UTC+2 schrieb Anonymous:
>
> If I have a vector of data which I don't intend to ever change, should I
> represent it as a tuple rather than a 1d array? Is there any benefit above
> and beyond protecting against mutability? Is it more efficient?
>
