[awm]

> They can 'compile' in an 'optimized manner' using something else ...

Thats actually the issue Julia attempts to solve as highlighted in the article. There are many languages that you can use to express ideas, but when you need the idea to run as fast as possible, you have to rewrite in C/Fortran/etc and then add bindings to to the language.

Julia lets you write inline C, which I believe means that you can still express algorithms simply, but with some parts (trivially) optimized. (Not a huge julia user, but thats my take on it)

[mauro3]

Whilst Julia's foreign function interface is indeed good and it is really easy to call into C, the point is that Julia itself is as fast as C. So you don't need to write any C code to get performance, instead just tune the bottlenecks in Julia itself.

For instance, the standard library of Julia [1] is written in Julia itself (and is very performant) and only calls into external C or Fortran libraries where there are well established code-bases (e.g. BLAS, FFTW). Compare this to, e.g., Python or R where much of the standard library is written in C.

[1] https://github.com/JuliaLang/julia/tree/master/base

[edblarney]

I understand that - but wouldn't a really smart compiler be able to do the same thing?

And if you have to be able to write 'some parts' in C for speed ... then why not just use C? And have nice libs for whatever you are doing?

Or intelligently distribute it across processes?

I find it hard to believe that the 'financial industry' has performance requirements that have never been encountered in the entire rest of high tech before ...

[rifung]

> wouldn't a really smart compiler be able to do the same thing?

Well compilers are tied to the language they need to compile so language choice does still matters. As an extreme example, GHC can be much more aggressive with inlining because it's pure, whereas most other languages don't provide the same guarantees.

> And if you have to be able to write 'some parts' in C for speed ... then why not just use C? And have nice libs for whatever you are doing?

Yes I think they could do that, but then they'd need people to write and maintain those libraries. My understanding is they want to find something which strikes a better balance between productivity and speed. Having to write your own libraries to do everything is likely considered a productivity loss.

[awm]

Ah, I see. I'm no expert, but I'd argue the response is "Maybe".

I think this is what blaze and pypy try to do with python: they compile it so that certain things are optimized. The harder part is that you have to generally hint for the compiler to truly be the best...

WRT to just why not use C, python/R/Julia are MUCH easier to prototype in than C (in my opinion, having used those languages). The "grail" is to be able to prototype quickly, and then identify the hotspots to speed up (which, in Julia's case, would be just inlining with C code).