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> With regards to numerical programming, it's obviously already far ahead of swift, and IMO much better placed to beat it in the long run. Julia's secret sauce

> With regards to numerical programming, it's obviously already far ahead of swift, and IMO much better placed to beat it in the long run. Julia's secret sauce is LLVM. Considering the guy behind Swift also made LLVM, I'm inclined to think that Swift will come out ahead.

omaranto 8y ago
I'd say LLVM is an important part of Julia's success but not the whole story: it is also a very well designed language.
- renox 8y ago
  Really? The 1-based array is really a mistake.. They should have followed Ada: allow any starting index and provide keywords to access the first/last element of the array. All this by default, otherwise it doesn't matter.
  - omaranto 8y ago
    I don't pretend to understand the feelings of people who care about this issue strongly, so I won't defend the choice vigorously. But I would guess they did that because their target demographic were Fortran and Matlab users, all of whom use 1-based indices, as is usual in math books. At any rate, in Julia you rarely need to write the start index explicitly (because of functions like eachindex) and can easily start your indices anywhere you like if you feel strongly about it. (I know that to you specifically the fact that the ability to choose the starting index is not builtin means it doesn't matter, but I point it out for others who might not know that it's easy to do in Julia.)
byt143 8y ago
>Julia's secret sauce is LLVM That most certainly is not true. See this for example: https://arxiv.org/pdf/1810.09868.pdf https://arxiv.org/pdf/1810.09868.pdf
- adamnemecek 8y ago
  XLA is built on LLVM I think.
  - byt143 8y ago
    I don't think so(https://www.tensorflow.org/xla/developing_new_backend https://www.tensorflow.org/xla/developing_new_backend). And even if so, it's not relying on LLVM optimizations (for TPU) or the LLVM API. (which is why some of these had to be punted back up to the Julia optimizer for XLA, which was done in a third party package!). The point is that Julia's design, type system and multiple dispatch facilitates writing dynamic yet highly optimized code for a variety of backends, even those requiring static semantics (unlike LLVM). There is no way you can look at that paper (or the Flux ecosystem, or the prob programming languages or the SSA IR autodiff) and chalk up Julia's success to just LLVM.
  - KenoFischer 8y ago
    The GPU backend is (and so's the CPU backend, but that's currently too slow to get much information from). The TPU backend isn't (which is why we targeted XLA in the first place).

omaranto 8y ago

I'd say LLVM is an important part of Julia's success but not the whole story: it is also a very well designed language.

renox 8y ago
Really? The 1-based array is really a mistake.. They should have followed Ada: allow any starting index and provide keywords to access the first/last element of the array. All this by default, otherwise it doesn't matter.
- omaranto 8y ago
  I don't pretend to understand the feelings of people who care about this issue strongly, so I won't defend the choice vigorously. But I would guess they did that because their target demographic were Fortran and Matlab users, all of whom use 1-based indices, as is usual in math books. At any rate, in Julia you rarely need to write the start index explicitly (because of functions like eachindex) and can easily start your indices anywhere you like if you feel strongly about it. (I know that to you specifically the fact that the ability to choose the starting index is not builtin means it doesn't matter, but I point it out for others who might not know that it's easy to do in Julia.)

byt143 8y ago

>Julia's secret sauce is LLVM That most certainly is not true. See this for example: https://arxiv.org/pdf/1810.09868.pdf https://arxiv.org/pdf/1810.09868.pdf

adamnemecek 8y ago
XLA is built on LLVM I think.
- byt143 8y ago
  I don't think so(https://www.tensorflow.org/xla/developing_new_backend https://www.tensorflow.org/xla/developing_new_backend). And even if so, it's not relying on LLVM optimizations (for TPU) or the LLVM API. (which is why some of these had to be punted back up to the Julia optimizer for XLA, which was done in a third party package!). The point is that Julia's design, type system and multiple dispatch facilitates writing dynamic yet highly optimized code for a variety of backends, even those requiring static semantics (unlike LLVM). There is no way you can look at that paper (or the Flux ecosystem, or the prob programming languages or the SSA IR autodiff) and chalk up Julia's success to just LLVM.
- KenoFischer 8y ago
  The GPU backend is (and so's the CPU backend, but that's currently too slow to get much information from). The TPU backend isn't (which is why we targeted XLA in the first place).