@Bill Hart
small note on your example: you don't return anything, which is why LLVM's
code elimination jumps in and removes your whole function body, which
results in a very fast "addition" :P
Even if you return c, LLVM is smart enough to just precalculate the value
resulting in doit() = 30 as the method definition.
Even having doit take a and b as arguments results in doit(a,b) = a+b.
Even this:
function doit(n)
c = 0
for i = 1:n
c += i
end
end
gets turned into a constant expression.
It's actually not that easy, to get the plus benchmark, as it gets heavily
optimized.
I think something like this will approximate the time needed better:
function doit(n)
bench = 0.0
for i = 1:n
a = rand()
tic()
a+a
t = toq()
bench += t
end
bench
end
doit(10^6)
@show doit(10^6)
doit(10 ^ 6) = 0.09192137800017194
This is actually pretty awesome about Julia, as even someone like me with
only a small background in computer science is able to find out all these
things.
(via @code_llvm, @code_native, etc)
Am Dienstag, 24. Februar 2015 10:13:52 UTC+1 schrieb [email protected]:
>
> Hello,
>
> I am a beginner to Julia and would like to try out some features. I would
> like to improve performances of some bottleneck functions by translating
> them into C.
> A simple example is :
> main.jl
>
> a ::Int64 = 20
> b ::Int64 = 10
> ccall(:do_sum, Int64, (Int64, Int64), a, b)
>
>
>
> test.c
> #include <stdio.h>
>
> int do_sum (int a,int b)
> {
> int c;
> c = a+b;
> printf("f\n:",c);
> return c;
> }
>
> where should I put the C file ?
>
> Thanks,
> G.
>
>
