# Benchmark for latest julia?

**URL:** https://discourse.julialang.org/t/benchmark-for-latest-julia/5991
**Category:** Community
**Tags:** question
**Created:** [September 20, 2017, 2:47am UTC](https://discourse.julialang.org/t/benchmark-for-latest-julia/5991 "2017-09-20T02:47:24Z")
**Posts on this page:** 1
**Showing post:** 29

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### Author: ![JackDevine](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/jackdevine/32/1048_2.png) [@JackDevine](https://discourse.julialang.org/u/JackDevine)
#### Post date: [September 27, 2017, 7:52am UTC](https://discourse.julialang.org/t/benchmark-for-latest-julia/5991/29 "2017-09-27T07:52:58Z")

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When we had the “Why not numba?” discussion, I made a graph comparing Julia to Numba. Maybe it would be nice to add Numba alongside python? It is pretty easy, because all you have to do is decorate the functions with `@autojit`. It’s just a suggestion though.

> [@Julia motivation: why weren't Numpy, Scipy, Numba, good enough?](https://discourse.julialang.org/t/julia-motivation-why-werent-numpy-scipy-numba-good-enough/2236/42):
>
> This conversation may be a little dead, but I was also stumped by the “why not numba?” question when I gave a talk on Julia recently. So, I took the benchmarks from test/perf/micro and for all of the python benchmarks I added @jit decorations to the functions. This worked fine except for parse\_int which gave me some error that I didn’t understand. After running the benchmarks, this is what I get: [image] [image] I am on a 2013 macbook pro with an intel i7 quad core (2.6GHz) and 16Gb of ram. T…

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