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Hmm, I would guess that if somebody doesn't already know about the Julia language, this wouldn't be a very interesting page (which is fine). So this is a great

Hmm, I would guess that if somebody doesn't already know about the Julia language, this wouldn't be a very interesting page (which is fine). So this is a great title! In case you're curious, the website is https://julialang.org/ https://julialang.org/ > Julia is a high-level, high-performance dynamic programming language for numerical computing. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library.

yesenadam 9y ago
Thanks. Well, I've heard of the language Julia, but didn't connect the title with it as it sounded like a person, robot or something.. Mm sounds good. Is it as fast as, say, C?
- rattray 9y ago
  Ah, gotcha. Maybe it didn't initially say "Julia Lang"; it does now, perhaps thanks to your comment.
  - yesenadam 9y ago
    Hehe no. It said "Julia Lang v0.7" but I'm not insider enough to think of Julia the language when I read that. I wondered "Who is Julia Lang? Hmm must be some software?" etc
    - doodpants 9y ago
      I'd think that most regular readers of Hacker News would naturally interpret "____ Lang" as referring to a programming language, but clearly there's some ambigutity. I would have the opposite problem; if a Hacker News item was titled "Fred Lang Has Died", my first thought would be: "I didn't even know there was a programming language called Fred. It must have died from obscurity." :-)
      - yesenadam 9y ago
        I guess the problem is with the name Julia, which is lovely but you have to specify that you mean the language—no-one ever mistakes what "C++" refers to.
- jernfrost 9y ago
  When you write code in a C like fashion it can often get as fast or faster than C. Of course it has the disadvatnage of all jit languages that it will likely have slower startup. But when you crunch big chunks of data it should be able to compete with C. Actually want to qualify this a bit. Nothing gets automatically fast by just picking a fast language. That Julia is close to C in performance mainly means it is possible to achieve this through optimization without too much effort. However you still need to have some idea of what makes something slow or fast in Julia. Personally I find it very easy to optimize Julia code because you can quickly see how the JIT transforms an individual function and what you may need to change.
  - yesenadam 9y ago
    Ok thanks. Lately been using Cython with its near-C speed and Python convenience. Just quickly had a look at Julia wiki page, seems like a fast Python, is that fair?
    - jernfrost 9y ago
      Superficially you could say that, but the design of Julia is such that it is much easier for you to grasp what is going on at the lower levels and the performance implications of the code you write. Python is much more of a black box, performance wise. Also something like Cython is radically different from Julia. Cython is compiled to C code. Julia is based on JIT so while you have an interactive session in the REPL, functions are continously compiled as you make them.
    - ChrisRackauckas 9y ago
      Python doesn't have multiple dispatch though. It's central to making Julia's type system fast. I.e., it's not just loops that are fast, but also code that uses Julia-defined data structures are fast. Additionally, you can write generic code and have it automatically compile fast code for large classes of types. Also, the whole Base library is written in Julia, mostly generically, so this will be compatible with your user-defined data structures as well. Then finally, the kicker is that Julia, especially on v0.7, doesn't optimize functions separately: it optimizes them together. It will inline small functions into others, perform interprocedural optimizations and utilize compilation-time constants, etc. Thus when the code is Julia all the way down, it can and will compile everything together to optimize it a lot more than functions compiled separately, giving a lot more performance benefits. When you add in the macros to turn off things like bounds checks and adding in explicit SIMD, you truly get to C-level of performance and many times beyond because your code is so architecturally and vertically optimized (it's like you put on the flags to say "compile code that only works for this current machine with this current codebase", and it can safely make this assumption because it's JITing). Because of this, it goes much further than Cython, and this also makes the type system and multiple dispatch central to the language. So I would say at a surface level it's "fast Python" (or "more productive C", that's how I usually think of it). However, at a deeper level the type system is so central that larger software architectures will be different to accommodate this multiple dispatch style as opposed to OOP.

yesenadam 9y ago

Thanks. Well, I've heard of the language Julia, but didn't connect the title with it as it sounded like a person, robot or something.. Mm sounds good. Is it as fast as, say, C?

rattray 9y ago
Ah, gotcha. Maybe it didn't initially say "Julia Lang"; it does now, perhaps thanks to your comment.
- yesenadam 9y ago
  Hehe no. It said "Julia Lang v0.7" but I'm not insider enough to think of Julia the language when I read that. I wondered "Who is Julia Lang? Hmm must be some software?" etc
  - doodpants 9y ago
    I'd think that most regular readers of Hacker News would naturally interpret "____ Lang" as referring to a programming language, but clearly there's some ambigutity. I would have the opposite problem; if a Hacker News item was titled "Fred Lang Has Died", my first thought would be: "I didn't even know there was a programming language called Fred. It must have died from obscurity." :-)
    - yesenadam 9y ago
      I guess the problem is with the name Julia, which is lovely but you have to specify that you mean the language—no-one ever mistakes what "C++" refers to.
jernfrost 9y ago
When you write code in a C like fashion it can often get as fast or faster than C. Of course it has the disadvatnage of all jit languages that it will likely have slower startup. But when you crunch big chunks of data it should be able to compete with C. Actually want to qualify this a bit. Nothing gets automatically fast by just picking a fast language. That Julia is close to C in performance mainly means it is possible to achieve this through optimization without too much effort. However you still need to have some idea of what makes something slow or fast in Julia. Personally I find it very easy to optimize Julia code because you can quickly see how the JIT transforms an individual function and what you may need to change.
- yesenadam 9y ago
  Ok thanks. Lately been using Cython with its near-C speed and Python convenience. Just quickly had a look at Julia wiki page, seems like a fast Python, is that fair?
  - jernfrost 9y ago
    Superficially you could say that, but the design of Julia is such that it is much easier for you to grasp what is going on at the lower levels and the performance implications of the code you write. Python is much more of a black box, performance wise. Also something like Cython is radically different from Julia. Cython is compiled to C code. Julia is based on JIT so while you have an interactive session in the REPL, functions are continously compiled as you make them.
  - ChrisRackauckas 9y ago
    Python doesn't have multiple dispatch though. It's central to making Julia's type system fast. I.e., it's not just loops that are fast, but also code that uses Julia-defined data structures are fast. Additionally, you can write generic code and have it automatically compile fast code for large classes of types. Also, the whole Base library is written in Julia, mostly generically, so this will be compatible with your user-defined data structures as well. Then finally, the kicker is that Julia, especially on v0.7, doesn't optimize functions separately: it optimizes them together. It will inline small functions into others, perform interprocedural optimizations and utilize compilation-time constants, etc. Thus when the code is Julia all the way down, it can and will compile everything together to optimize it a lot more than functions compiled separately, giving a lot more performance benefits. When you add in the macros to turn off things like bounds checks and adding in explicit SIMD, you truly get to C-level of performance and many times beyond because your code is so architecturally and vertically optimized (it's like you put on the flags to say "compile code that only works for this current machine with this current codebase", and it can safely make this assumption because it's JITing). Because of this, it goes much further than Cython, and this also makes the type system and multiple dispatch central to the language. So I would say at a surface level it's "fast Python" (or "more productive C", that's how I usually think of it). However, at a deeper level the type system is so central that larger software architectures will be different to accommodate this multiple dispatch style as opposed to OOP.