Put a function around the code and everything works as expected:
function perf()
nsamples = 1000000;
x = linspace(0, 5, nsamples);
y = zeros(nsamples);
noise = zeros(nsamples);
a = rand(); a = cos(0.0); a = 1.5*2.5; a = 1.5+2.5;
println("\nBrutal-force loops, 100 times:")
for m = 1:100
morenoise = rand();
for n = 1:nsamples
noise[n] = rand();
y[n] = cos(2*x[n]+5) + noise[n] + morenoise;
end
end
end
@time(perf())
*Brutal-force loops, 100 times:*
* 2.964993 seconds (151.75 k allocations: 21.837 MB, 0.68% gc time)*
On Monday, July 11, 2016 at 8:57:05 AM UTC+2, Zhong Pan wrote:
>
> Hi,
>
> For work I really desired a language for numerical / data processing
> that's as easy and capable as Python but still fast running loops. That's
> why I am very excited when I noticed Julia. First allow me to me say,
> fabulous work! I really appreciate the effort of those who make open-source
> software great for everyone.
>
> I did a simple & naive benchmark involving brutal-force loops with some
> floating-point calculations on Windows (have to use Windows for work). I
> measured execution time in Python, Julia, VC++, C#.NET, Java, and Matlab.
> To my surprise, Julia is not only much slower than VC++ in this test, but
> it's much slower than C#.NET, Java, and even Matlab.
>
> Please see details of the test in the attached PDF file. Could someone
> help me out here - did I make an amateur mistake or miss something? Was
> Julia slow because of Windows? Or was it just the loops that's slow (as
> this "benchmark" really didn't test anything else)? I really want to make
> Julia my go-to language in work, so any suggestion is appreciated.
>
> Cheers.
> -Zhong
>
>
>
