It's primarily because you're using 64-bit integers in Julia and 32-bit integers in all the other cases. If you change the C code to use `long` instead of `int`, the timings are about the same. You're also just summing result in C but accumulating it in Python and Julia and summing later, which is less efficient – although it ends up not making much difference here.
It's difficult to use 32-bit integer arithmetic in Julia on a 64-bit machine, but you usually don't want to. It's sometimes a performance hit like it is here, but that's pretty rare, and 2 billion really just isn't big enough for a lot of pretty mundane things you might use integers for (Bill Gates can't represent their net worth with a 32-bit int!). 64-bit integers on the other hand are big enough for any mundane counting task – you don't hear people talking about quintillions of things, ever. Still, it would be nice to be able to compile and/or run Julia in 32-bit mode on a 64-bit machine and vice versa. It's clearly not appropriate for benchmarking, but this Julia one-liner computes the same thing much more efficiently: julia> test2(n=20000) = sum(primes(ifloor(n*log(n*log(n))))[1:n]) test2 (generic function with 2 methods) julia> @time test2() elapsed time: 0.004566614 seconds (363112 bytes allocated) 2137755325 The primes function<https://github.com/JuliaLang/julia/blob/master/base/primes.jl>uses a BitArray and the Sieve of Atkin to find all primes up to the given number. Since ifloor(n*log(n*log(n))) is an upper bound on the nth prime, this always produces at least n prime values and returns their sum. On Tue, Jan 14, 2014 at 5:05 PM, Eric Davies <iam...@gmail.com> wrote: > Running test2() once before running @time test2() (to force compilation) > results in a 13% performance improvement on my system. > > > On Tuesday, 14 January 2014 15:32:16 UTC-6, Przemyslaw Szufel wrote: >> >> Dear Julia users, >> >> I am considering using Julia for computational projects. >> As a first to get a feeling of the new language a I tried to benchmark >> Julia speed against other popular languages. >> I used an example code from the Cython tutorial: >> http://docs.cython.org/src/tutorial/cython_tutorial.html [ the code for >> finding n first prime numbers]. >> >> Rewriting the code in different languages and measuring the times on my >> Windows laptop gave me the following results: >> >> Language | Time in seconds (less=better) >> >> Python: 65.5 >> Cython (with MinGW): 0.82 >> Java : 0.64 >> Java (with -server option) : 0.64 >> C (with MinGW): 0.64 >> Julia (0.2): 2.1 >> Julia (0.3 nightly build): 2.1 >> >> All the codes for my experiments are attached to this post (Cython i >> Python are both being run starting from the prim.py file) >> >> The thing that worries me is that Julia takes much much longer than >> Cython ,,, >> I am a beginner to Julia and would like to kindly ask what am I doing >> wrong with my code. >> I start Julia console and use the command include ("prime.jl") to >> execute it. >> >> This code looks very simple and I think the compiler should be able to >> optimise it to at least the speed of Cython? >> Maybe I my code has been written in non-Julia style way and the compiler >> has problems with it? >> >> I will be grateful for any answers or comments. >> >> Best regards, >> Przemyslaw Szufel >> >
