Re: [Gretl-users] When Julia is faster than C and Gretl
Thank you all for your replies. It's amazing to see what JIT can
actually do.
But given that, at least in my view, gretl's natural competitors are R
and Eviews (and to a certain degree Octave/MATLAB) rather than
specialized software for numerical simulations or related stuff, I am
happy that gretl beats R, Octave and even Python (ok w.o. JIT) in this
task ;-)
Btw, I've just received the following link which may be of interest for
further competitions: https://modelingguru.nasa.gov/docs/DOC-2625
Best,
Artur
Am 10.02.2018 um 16:36 schrieb Sven Schreiber:
Am 10.02.2018 um 15:01 schrieb Riccardo (Jack) Lucchetti:
> On Sat, 10 Feb 2018, Artur Tarassow wrote:
> IMHO, this is one of the cases when teh JIT approach gives a huge
> performance boost;
I agree.
> gretl took 1.454405 sec.
> 2.718006
On my oldish machine for input 10^6:
- gretl takes 2.7 secs
- Python (with numpy.random.uniform): 4.8 secs
- Python + Numba's just-in-time compilation (using the @jit decorator):
0.17 secs!
I don't have Julia here, but to compare this to Artur's numbers who used
10^7:
- gretl: 28 secs
- Python + Numba jit: 0.48 secs
So if Julia really is 100x faster than gretl here, Python+jit might be
just a little slower, being only 60x faster. (Note however that I only
have the free numba which uses llvmlite in the background I think;
Numbapro might be even faster, don't know.)
cheers,
sven
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