3 ms·

No, I say that Cython and Numba made an engineering tradeoff that reduces their performance and flexibility in exchange for the ability to compile their codes s

No, I say that Cython and Numba made an engineering tradeoff that reduces their performance and flexibility in exchange for the ability to compile their codes separately, whereas Julia needs to compile dependently. You are the one that is inferring that means it's inferior. There are cases where this ability to easily reduce compile time can be useful, and there are cases where the improved flexibility and performance is useful. But this is the tradeoff that is made in a concrete form, and the value judgement of whether it's a good one is for you to make. Besides, please show me that 10 lines of Cython that shows it can AD and throw numbers with uncertainties through SciPy's ODE solvers since it's just as flexible as Julia. It should take less characters to write than your previous response!

mlthoughts2018 8y ago
This is a quote from the link: > “It's not hard to get a loop written in terms of simple basics (well okay, it is quite hard but I mean "not hard" as in "give enough programmers a bunch of time and they'll get it right"). Cool, we're all the same. And microbenchmark comparisons between Cython/Numba/Julia will point out 5% gains here and 2% losses here and try to extrapolate to how that means entire package ecosystems will collapse into chaos, when in reality most of this is likely due to different compiler versions and go away as the compilers themselves update. So let's not waste time on these single functions. Let's talk about large code bases. Scaling code to real applications is what matters. Here's the issue: LLVM cannot optimize a Python interpreter which sits in the middle between two of your optimized function calls, and this can HURT.” You seem to be disingenuously recasting what’s written in the link to serve shifting purposes, to the point that I am skeptical you’ve even got a coherent claim here in your comments.
- Certhas 8y ago
  1. You're talking to the author. 2. The quoted text talks exactly about separate compilation, like the comments above do. So it's really unclear where you take offense. Maybe read it again carefully? 3. I have championed numba for a long time, and am now happy to jump to Julia, for pretty much those reasons. I have implemented time critical parts of the code in numba, and that has forced me to reimplement a whole bunch of standard algorithms rather than relying on library implementations. Need sparse matrices in your hot loop? well you can't just call scipy sparse. That's the fundamental disaster of numba. You sit in the middle of the most amazing ecosystem and you can't use it.
  - mlthoughts2018 8y ago
    I’m not clear on why you being the author is supposed to matter. There’s a direct, unambiguous quote (of your own writing) that disagrees with what you are saying here. I don’t care who wrote it, only that your opinions here in the comments are not consistent with the actual linked post. Authors can have inconsistent opinions, and can sometimes try to retroactively reinterpret something they wrote to serve a different purpose later on. That is happening here. It’s not a big deal; I’m just saying for this reason I don’t agree with your claims about my comments, nor your overall comments about numba (even after re-reading your link and comments and deliberately reflecting on them).
    - Certhas 8y ago
      I'm not the author, the guy you're replying to is. The article that you quote says that crossing function barriers and joint compilation are issues with numba and cython. So does the comment you were replying to. You've failed to articulate where the perceived discrepancy is.

mlthoughts2018 8y ago

This is a quote from the link: > “It's not hard to get a loop written in terms of simple basics (well okay, it is quite hard but I mean "not hard" as in "give enough programmers a bunch of time and they'll get it right"). Cool, we're all the same. And microbenchmark comparisons between Cython/Numba/Julia will point out 5% gains here and 2% losses here and try to extrapolate to how that means entire package ecosystems will collapse into chaos, when in reality most of this is likely due to different compiler versions and go away as the compilers themselves update. So let's not waste time on these single functions. Let's talk about large code bases. Scaling code to real applications is what matters. Here's the issue: LLVM cannot optimize a Python interpreter which sits in the middle between two of your optimized function calls, and this can HURT.” You seem to be disingenuously recasting what’s written in the link to serve shifting purposes, to the point that I am skeptical you’ve even got a coherent claim here in your comments.

Certhas 8y ago
1. You're talking to the author. 2. The quoted text talks exactly about separate compilation, like the comments above do. So it's really unclear where you take offense. Maybe read it again carefully? 3. I have championed numba for a long time, and am now happy to jump to Julia, for pretty much those reasons. I have implemented time critical parts of the code in numba, and that has forced me to reimplement a whole bunch of standard algorithms rather than relying on library implementations. Need sparse matrices in your hot loop? well you can't just call scipy sparse. That's the fundamental disaster of numba. You sit in the middle of the most amazing ecosystem and you can't use it.
- mlthoughts2018 8y ago
  I’m not clear on why you being the author is supposed to matter. There’s a direct, unambiguous quote (of your own writing) that disagrees with what you are saying here. I don’t care who wrote it, only that your opinions here in the comments are not consistent with the actual linked post. Authors can have inconsistent opinions, and can sometimes try to retroactively reinterpret something they wrote to serve a different purpose later on. That is happening here. It’s not a big deal; I’m just saying for this reason I don’t agree with your claims about my comments, nor your overall comments about numba (even after re-reading your link and comments and deliberately reflecting on them).
  - Certhas 8y ago
    I'm not the author, the guy you're replying to is. The article that you quote says that crossing function barriers and joint compilation are issues with numba and cython. So does the comment you were replying to. You've failed to articulate where the perceived discrepancy is.