The reason I did readdlm("numba_times.dat")[10:end] discarding the first
times is that at least the very first one is much much higher that the
others.
It seems that Numba also performs better after a warm-up.
Therefore, to be fair with Numba, we must discard the first function call
time as we often do with Julia.
Using this commit
<https://github.com/cdsousa/Comparison-Programming-Languages-Economics/tree/numba2runs>
which runs the main function twice for the Numba test, I get times around
this:
1st run elapse time = is 4.60552310944
2nd run elapse time = is 4.24891805649
which I think is significant.
On Tuesday, June 17, 2014 5:21:54 PM UTC+1, Cristóvão Duarte Sousa wrote:
>
> I've just done measurements of algorithm inner loop times in my machine by
> changing the code has shown in this commit
> <https://github.com/cdsousa/Comparison-Programming-Languages-Economics/commit/4f6198ad24adc146c268a1c2eeac14d5ae0f300c>
> .
>
> I've found out something... see for yourself:
>
> using Winston
> numba_times = readdlm("numba_times.dat")[10:end];
> plot(numba_times)
>
>
> <https://lh6.googleusercontent.com/-m1c6SAbijVM/U6BpmBmFbqI/AAAAAAAADdc/wtxnKuGFDy0/s1600/numba_times.png>
> julia_times = readdlm("julia_times.dat")[10:end];
> plot(julia_times)
>
>
> <https://lh4.googleusercontent.com/-7iprMnjyZQY/U6Bp8gHVNJI/AAAAAAAADdk/yUgu8RyZ-Kw/s1600/julia_times.png>
> println((median(numba_times), mean(numba_times), var(numba_times)))
> (0.0028225183486938477,0.0028575707378805993,2.4830103817464292e-8)
>
> println((median(julia_times), mean(julia_times), var(julia_times)))
> (0.0028240440000000004,0.0034863882123824454,1.7058255003790299e-6)
>
> So, while inner loop times have more or less the same median on both Julia
> and Numba tests, the mean and variance are higher in Julia.
>
> Can that be due to the garbage collector being kicking in?
>
>
> On Monday, June 16, 2014 4:52:07 PM UTC+1, Florian Oswald wrote:
>>
>> Dear all,
>>
>> I thought you might find this paper interesting:
>> http://economics.sas.upenn.edu/~jesusfv/comparison_languages.pdf
>>
>> It takes a standard model from macro economics and computes it's solution
>> with an identical algorithm in several languages. Julia is roughly 2.6
>> times slower than the best C++ executable. I was bit puzzled by the result,
>> since in the benchmarks on http://julialang.org/, the slowest test is
>> 1.66 times C. I realize that those benchmarks can't cover all possible
>> situations. That said, I couldn't really find anything unusual in the Julia
>> code, did some profiling and removed type inference, but still that's as
>> fast as I got it. That's not to say that I'm disappointed, I still think
>> this is great. Did I miss something obvious here or is there something
>> specific to this algorithm?
>>
>> The codes are on github at
>>
>> https://github.com/jesusfv/Comparison-Programming-Languages-Economics
>>
>>
>>
