A significant part of my time over the last 3 years has been devoted to a project entirely developed in Julia for precisely this reason:
- the problem was so complicated that we anticipated having to test several ways to solve it => efficiency in development
- most of the solution methods require lots of heavy computations => efficiency in compute power
We originally had to convince everyone involved that using Julia for this was a good idea. But now, 3 years later, we have a mini eco-system of ~10 packages (most of which are closed-source, but for instance Eikonal.jl came out of it too). And it is clear to everybody that this was the best choice[1].
I’m not so sure about this. For example, in the project mentioned above, some of the work involved running computations on GPU clusters. I’m very far from being a Numba expert, but I doubt it would have been as easy to do with Python/Numba than with Julia.
In particular, a better choice than Python + C++, which would otherwise have been the default choice, but retrospectively a terrible one ↩︎