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Comment by Q6T46nT668w6i3m | Hacker News Reader

They should be similar if they both rely on standard LLVM optimizations. I assume both have their own optimization passes on some mid-level IR but I personally don’t know whether one or the other has any particular language features that dramatically improve performance over the other. I’d like to hear more!

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I was just saying that getting really good performance out of LLVM with Julia isn't always obvious. This isn't just a Julia thing. People starting with Python + Numpy have issues with speed when they don't take advantage of the vector primitives and drop into regular Python loops for part of the code. First time Haskell users are frequently confused when it is significantly slower to parse text than Perl as one of the default string libraries is really slow and not recommended even though it is what a new user would think should be the first choice.

Overall I'm excited for Julia, but have a lot to learn. As far as Octave/Scilab type applications go, I find them useful for small systems where I need to make sure the math of what I'm doing is correct before writing it in something like Julia or Python + Numpy. Excel is also surprisingly good for the really simple stuff (Ex: inverting a small matrix and doing some matrix multiplications).

I assume they're referring to cases like this [1].

A few years ago I think there were some performance regressions in Julia relative to JIT'd Python (which, using LLVM like Julia, is way faster than normal Python). This seems to be mostly fixed now.

In the example above, the OP was getting 2ms in Numba vs 8ms in Julia three years ago, and recommendations from Julia folks were able to get the latter down to parity with an equivalent algorithm or 386us with an improved algorithm.

On my laptop today, I seem to get 2.0ms in Numba, 2.7ms with Julia 1.0, and 80us using the improved algorithm in Julia 1.0 (and, just to drive home the importance of compilation, 780ms in Python without Numba).

[1] https://old.reddit.com/r/Julia/comments/3x6tg9/why_is_this_l...

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