[ced]

Julia boasts performance as fast as that of languages like C or Fortran, and is still simple to learn.

I think the greatest benefit is that Julia code is both high-performance and (mostly) high-level, which makes it easy to change. I don't mind implementing a completely-specified algorithm in C or Fortran, but making significant changes to these code bases is simply much more work than in languages like Python or Julia.

[jjtheblunt]

I agree in sentiment but lately keep finding Python fraudulent in that regard: folks write complex messes in it because of its deficiencies, or to just be dramatic.

[vanderZwan]

However, Julia isn't Python - although I don't know enough about the latter to comment on what deficiencies you are referring to, nor do I know if Julia addresses these.

[ska]

see also lisp.

[ced]

Yes; Julia isn't unique in being a modern high-level high-performance language. But it's the only one I know that explicitly targets numerical computing, and it chooses its trade-offs accordingly.

[ska]

Agreed, that wasn't meant as a knock on julia. Just pointed out there is a long history behind the approach. Part of lisp's role in the "last AI bubble (TM)" was how natural it was to express certain types of problems in it. Julia is clearly targeting a similar advantage in other areas.

[philip1209]

Native matrix syntax and operations is a huge plus, IMHO.

[ska]

This is true - I once wrote something like that in a common lisp but never got it past the half-assed but useful.

Similarly (but painfully and never completely successful), see all the c++ expression template approaches to linear algebra. After 15 years or so some of them are quite usable but retain some of the pain.