4 ms·

i am always conflicted when using python, while it is unusually productive to hack up something in python i always keep on worrying about performance. I wish i

• kensai 9y ago
  This is a recurring theme in the field. You could consider Julia, especially now that has received some serious funding. But its problem is that it's still not v1.0, there may be serious changes before that milestone.
  • hobofan 9y ago
    I don't think that Julia's funding is a good reason to pick it up, maybe even the opposit of that.
  • sn9 9y ago
    Julia is really going to explode once it hits 1.0 status. I feel like that's the main thing holding people back from contributing to the library ecosystem.
    • marmaduke 9y ago
      Are there any good examples of Julia doing serious work that can't be done in Python? With libraries like Numba in Python I just can't see the reason for Julia (aside from the neat type system but that's not enough to switch imo)
      • one-more-minute 9y ago
        Take a look at Celeste [1], which ran on the Cori supercomputer. At that level you really need high performance not just for numerics but also for IO and heavily custom data structures, which was previously only possible in Fortran and C++. [1]: https://juliacomputing.com/case-studies/intel-astro.html https://juliacomputing.com/case-studies/intel-astro.html
• marmaduke 9y ago
  Most of the time it's not an issue. Especially in numerical work it is though, and libs like numba have your back.