However, Numba is quite limited because it only works well for mathematical code (it is not able to apply its optimizations to complex objects, like lists of dictionaries), while on the other side Julia's compiler applies its optimizations to everything.
[1] https://discourse.julialang.org/t/comparing-python-julia-and...
* Julia can do loop fusion when broadcasting, while numpy can't, meaning numpy uses a lot more memory during complex operations. (Numba can handle loop fusion, but it's generally much more restrictive.)
* A lot of code in real applications is glue code in Python, which is slow. I've literally found in some applications that <5% of the time was spent in numpy code, despite that being 90% of the code.
That said, if your code is mostly in numba with no pure python glue code (not just numpy), you probably won't see much of a difference.
EDIT: And last time I checked, Numpy only parallelizes calls to supplied linear algebra routines, and only if you have the right library installed. A simple vector arithmetic operation like a + b will execute on one core only.
Most people use a ton of numpy and scipy. It turns out that phrasing things as array operations with numpy operators is quite natural in this field, including for things like galaxy merger simulations.
I work, in particular, on asteroid detection and orbit simulation, and it's all pretty much Python.
You can get very far with these approaches I python, but having these at the language level just has more potential for optimization and less friction.
The debugability of numba code is very limited and code coverage does ot work at all.
Having a high level language that has scientific use at its core is just great.
Python has the maturity and community size on its side, but Jul is catching up on that quickly.
“It turns out that phrasing things as array operations with numpy operators is quite natural in this field”
But if A and B are numpy arrays, then A + B will calculate the elementwise sum on a single core only, correct? It will vectorize, but not parallelize. All large-scale computation is multi-core.
That's basically the answer.
