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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