[Mikeb85]

Julia is much, much faster than Python. For numeric code it's almost as fast as C++. They've used lots of cool tricks to become crazy fast on numeric workloads. It's also definitely got support for multithreading, clusters, etc...

[longqzh]

For numeric code, comparing to pure python makes no sense, since people use numpy+numba to do that. Is julia much much faster than numba? I don't think so :)

[adgjlsfhk1]

Julia is generally in the same ballpark as Numba (depending on the application they should be within 2%). The difference is that Julia is a full language, while Numba breaks if you try to use it with anything else.

[rfw300]

Yeah, Numba usually delivers on its performance promises when used right, but can be such a huge hassle oftentimes as to make it not really worth it.

[jjoonathan]

Yes, Numba is just barely less awful than managing a C build process.

Julia is a much better solution to the two language problem, here's hoping it can overcome the ecosystem inertia.

[jolux]

Probably not faster in an absolute sense but things like loops in Julia can be properly optimized and will sometimes be more readable than structuring your program entirely around NumPy constructs.

[longqzh]

For numpy, it's correct. But numba can optimize the loop. So optimized and readable loop is not an advantage of julia compare to numba.

[ChrisRackauckas]

Not in real-world contexts. This is spelled out in Julia for Biologists (https://arxiv.org/abs/2109.09973) which does the operation counting to show why using Numba with SciPy is still an order of magnitude slower in scientific operations like solving differential equations compared to Julia. An order of magnitude on widely used scientific analyses is pretty significant!

[dnautics]

yeah, then you try to use numba + scipy and then sure many things work but you're never too far away from a tableflip and wanting to curse your fucking life out.