Generating one by one or together is a small part of the execution time:
julia> @time rand(3*10^8);
1.281946 seconds (10 allocations: 2.235 GB, 0.23% gc time)
julia> r(n) = for i=1:n; rand(); end;
julia> @time r(3*10^8)
0.632389 seconds (6 allocations: 192 bytes)
The one by one is perhaps faster because it doesn't have the array
allocation overhead. So, it
-viral
On Saturday, January 23, 2016 at 6:53:38 AM UTC+5:30, Tim Holy wrote:
>
> Try
> @edit randperm(10)
> and see for yourself.
>
> My bet is that you could speed it up by generating all the random numbers
> you'll need in one go, rather than generating them one-by-one. Want to
> give it
> a shot?
>
> --Tim
>
> On Friday, January 22, 2016 02:54:51 PM Brian Lucena wrote:
> > I am running a simulation that requires me to generate random
> permutations
> > of size 300 million. I would ideally like to run this generation in a
> loop
> > 10K or 100K times. I am surprised that randperm is not faster in Julia
> than
> > it is. It seems to be considerably slower than the equivalent in R (and
> R
> > is clearly not known for speed) :)
> >
> > In Julia:
> >
> > *julia> **@time asd=randperm(300000000)*
> >
> > 43.437829 seconds (6 allocations: 2.235 GB, 0.01% gc time)
> >
> > *300000000-element Array{Int64,1}:*
> >
> >
> > In R
> >
> > > start = Sys.time()
> > >
> > > asd = sample(300000000)
> > >
> > > Sys.time()-start
> >
> > Time difference of 23.27244 secs
> >
> > Julia seems to be twice as slow!
> >
> > Any thoughts on why that is? Does randperm use the "knuth shuffle" or
> does
> > it use some other algorithm.
> >
> > Thanks,
> > B
>
