I'm not sure the equivalence is entirely accurate:
julia> a = Vector{Int}()
0-element Array{Int64,1}
julia> Base.summarysize(a)
0
julia> @time sizehint!(a,10_000_000)
0.000096 seconds (149 allocations: 76.304 MB)
0-element Array{Int64,1}
julia> Base.summarysize(a)
0
julia> @time b = zeros(Int,10_000_000);
0.037202 seconds (35 allocations: 76.295 MB, 64.13% gc time)
julia> Base.summarysize(b)
80000000
On Monday, November 16, 2015 at 1:10:36 AM UTC-8, Tomas Lycken wrote:
>
> sizehint! preallocates for you, with comparable cost to calling e.g.
> zeroes, but lets you treat the array semantically the same way as a
> non-preallocated one (but without the cost for reallocation). Hopefully,
> these comments highlight the differences between the various appraches:
>
> N = 10_000
> A = Array(Float64,0)
> sizehint!(A, 10_000) # this preallocates memory for 10k elements
> B = Array(Float64,0)
> C = zeros(10_000) # this also preallocates memory for 10k elements
>
> # now, A and C are pre-allocated, while B is not# however, A and B are
> semantically equivalent (0-length) vectors,
> # while C is already of length 10 000:
> println(length(A)) # 0
> println(length(B)) # 0
> println(length(C)) # 10000
>
> for i in 1:10_000
> push!(A, i) # no reallocation happens here, because we did it with
> sizehint!
> push!(B, i) # this will re-allocate B every now and then
> C[i] = i # can't use push! here, but must manually track index instead
> end
>
> I don't know what `dynamic` does in this context, and I can't find it in
> the docs, so can't help you there :)
>
> // T
>
> On Monday, November 16, 2015 at 2:07:13 AM UTC+1, Seth wrote:
>
> What happens if you use sizehint!() with dynamic()?
>>
>> On Sunday, November 15, 2015 at 3:35:45 PM UTC-8, Steven G. Johnson wrote:
>>>
>>> function prealloc(n)
>>> a = Array(Int, n)
>>> for i = 1:n
>>> a[i] = i
>>> end
>>> return a
>>> end
>>> function dynamic(n)
>>> a = Int[]
>>> for i = 1:n
>>> push!(a, i)
>>> end
>>> return a
>>> end
>>> @time prealloc(10^7);
>>> @time dynamic(10^7);
>>>
>>>
>>> On my machine, the preallocated version is 2.5–3x faster. A significant
>>> but not overwhelming margin.
>>>
>>
>
