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Has Julia's popularity in scientific computing and data science continued to grow? I haven't heard much about it recently.

ysofunny 2y ago
isn't julia just another brand-name (™,®, and ©) python? like anaconda? or possibly the R language???
- frakt0x90 2y ago
  Not at all? Totally different programming paradigm and performance. Certain communities pull towards Julia a lot more than others. Mostly I've seen scientific fields that require HPC but don't want to do everything in FORTRAN and C. Paging Chris Rackauckas!
- ForHackernews 2y ago
  No. It's not.
- jordanb 2y ago
  R is an open source version of S, which was a competitor to SAS. Julia, from when I looked at it years ago was trying like a new version of Matlab or Mathematica. It was very linear-algebra focused, and were trying to replace those packages plus Fortran. They had some gimmicks like an IDE that would render mathematical notion like TeX for your matrices. Python wasn't the obvious "Fortran killer" scientific language it is today. In fact it's arguably really weird that Python ended up winning that segment. In any case, I think Julia's been struggling since its inception.
  - bdjsiqoocwk 2y ago
    [flagged]
  - tomrod 2y ago
    I was in Austin while Travis Oliphant's wave from numpy led to Anaconda. After that we got to bring them in as consultants. It was wild talking to the team and hearing the inside-track dev info. It isn't a surprise to me that Python, as flexible and glue code as it is, became the Excel language of Scientific Computing.
    - nequo 2y ago
      What kinds of things did you hear from them?
      - tomrod 2y ago
        Mostly the vision and ideals which became Anaconda, conda, and miniconda, as well as the translation of ideas to use cases to implementations, and some ideas that came about later in other forms or libraries (numba, pytorch). Basically a mini/beta/in-progress version of Pycon each week.
  - wdkrnls 2y ago
    R and S are also very linear algebra focused. R developers just try to make C++ behave like R as much as possible when they need more speed. Hence, Rcpp. Otherwise, we prefer our LISPy paradise.
- minetest2048 2y ago
  Julia feels like a Matlab++ with its one based indexing and `function end` syntax Mojo is what you're thinking of
  - ls612 2y ago
    I primarily use MATLAB and what stops me from using Julia is the package management. Also the VSCode extension has weird performance problems when trying to debug Julia code.
    - adgjlsfhk1 2y ago
      > what stops me from using Julia is the package management. Can you expand on this? Julia's package manager IMO is one of the best parts of the language.
      - ls612 2y ago
        I hate with a burning passion having to manage packages myself. MATLAB comes preinstalled with everything and the kitchen sink.
        adgjlsfhk1 2y ago
        Fair enough. It probably would make sense to have a Conda like release of Julia that comes out every year with a broad but curated selection of packages.
        stillyslalom 2y ago
        Long ago, I trawled through Matlab's docs to come up with a set of Julia packages matching what's build into Matlab: https://discourse.julialang.org/t/julia-for-matlab-users-club/12872/8 https://discourse.julialang.org/t/julia-for-matlab-users-clu... I don't think you'd actually want to include each of those packages in a standard distro: does the average user really need to programmatically send emails or deal with Voronoi tessellations? Probably not, but I still think there's value in a batteries-included approach, especially when working with students.
cactusfrog 2y ago
I think it has correctness issues
- bdjsiqoocwk 2y ago
  [flagged]
- maximilianroos 2y ago
  Source?
  - minetest2048 2y ago
    https://yuri.is/not-julia/ https://yuri.is/not-julia/ HN discussion: https://news.ycombinator.com/item?id=31396861 https://news.ycombinator.com/item?id=31396861
    - ben_sisko 2y ago
      This link lays out the case in an exceptionally thorough and damning (for Julia) way.
    - cpfiffer 2y ago
      I think my general sense of this article is that all of these have been fixed. The language is relatively new, and the core devs are responsive. Using anything new comes with risks. I think the community appreciated a detailed and generally well-reasoned diagnosis, but at the same time these things are relatively easily addressed.
jarbus 2y ago
I use Julia regularly for experimental machine learning. It’s great for writing high performance, distributed code and even easier than Python for this kind of work, since I can optimize the entire stack in a single language. Not sure if it’s growing in popularity but it’s really solid for what it does
- nextos 2y ago
  Me too, and I'd like it to become mainstream. The major problem right now is that it doesn't have anything that is close to Torch or JAX in performance and robustness. Flux et al. are 90% there, but the last 10% requires a massive investment, and Julia doesn't have any corporate juggernaut funding development like Meta or Google. This is hurting Julia's adoption. The rest of the language is incredibly elegant, as there is no 2-language divide like in Python. Furthermore, it is really performant. With very little effort one can write code that is within 1.5-2x of C++, often closer. One possibility is that something like Mojo takes Julia's spot. Mojo has some of the advantages of Julia, plus very tight integration with Python, its syntax and its ecosystem. I would still prefer Julia, but this is something to keep in mind.
  - anonylizard 2y ago
    LLMs massively compound the advantage of existing popular languages, namely python. Any new learner will find it infinitely easier to use sonnet 3.5 to overcome the so called '2 language barrier' for python, while the lacking data for Julia becomes the real barrier. This issue will remain until LLMs get so smart they can maybe self-iterate and train on a given language. By then though, we'd likely get languages designed and optimized for LLMs.
    - SatvikBeri 2y ago
      For what it's worth I've found Claude Sonnet to work really well with Julia. One fun exercise was when a friend handed me a stack of well-written, very readable Python code that they were actually using. They were considering rewriting it in C, which would have been worth it if they could get a 10x speedup. I had Sonnet translate it to Julia, and it literally ran 200x faster, with almost identical syntax.
deleted 2y ago
[deleted]
currymj 2y ago
certainly yes in scientific computing, less so in ML/data science. there's much of the culture of scientific computing in economics -- lot of heavy numerical stuff in addition to the statistical modeling you might expect.
tkuraku 2y ago
I want to really like Julia. For me it felt like more work than python for simple stuff and not that much less work than c++ if you are trying to get the best performance. It is a cool language though.
SatvikBeri 2y ago
It's reasonably popular, growth has continued at a slow but steady pace. It's never going to become Python or anything but it's great in its niche. We use Julia in our hedge fund, it allows our researchers to write Python-like syntax while being very easy to optimize – compared to numpy code we've had a relatively easy time getting Julia to run 20x-1000x faster depending on the module, which has resulted in a very large reduction in AWS bills.

ysofunny 2y ago

isn't julia just another brand-name (™,®, and ©) python? like anaconda? or possibly the R language???

frakt0x90 2y ago
Not at all? Totally different programming paradigm and performance. Certain communities pull towards Julia a lot more than others. Mostly I've seen scientific fields that require HPC but don't want to do everything in FORTRAN and C. Paging Chris Rackauckas!
ForHackernews 2y ago
No. It's not.
jordanb 2y ago
R is an open source version of S, which was a competitor to SAS. Julia, from when I looked at it years ago was trying like a new version of Matlab or Mathematica. It was very linear-algebra focused, and were trying to replace those packages plus Fortran. They had some gimmicks like an IDE that would render mathematical notion like TeX for your matrices. Python wasn't the obvious "Fortran killer" scientific language it is today. In fact it's arguably really weird that Python ended up winning that segment. In any case, I think Julia's been struggling since its inception.
- bdjsiqoocwk 2y ago
  [flagged]
- tomrod 2y ago
  I was in Austin while Travis Oliphant's wave from numpy led to Anaconda. After that we got to bring them in as consultants. It was wild talking to the team and hearing the inside-track dev info. It isn't a surprise to me that Python, as flexible and glue code as it is, became the Excel language of Scientific Computing.
  - nequo 2y ago
    What kinds of things did you hear from them?
    - tomrod 2y ago
      Mostly the vision and ideals which became Anaconda, conda, and miniconda, as well as the translation of ideas to use cases to implementations, and some ideas that came about later in other forms or libraries (numba, pytorch). Basically a mini/beta/in-progress version of Pycon each week.
- wdkrnls 2y ago
  R and S are also very linear algebra focused. R developers just try to make C++ behave like R as much as possible when they need more speed. Hence, Rcpp. Otherwise, we prefer our LISPy paradise.
minetest2048 2y ago
Julia feels like a Matlab++ with its one based indexing and `function end` syntax Mojo is what you're thinking of
- ls612 2y ago
  I primarily use MATLAB and what stops me from using Julia is the package management. Also the VSCode extension has weird performance problems when trying to debug Julia code.
  - adgjlsfhk1 2y ago
    > what stops me from using Julia is the package management. Can you expand on this? Julia's package manager IMO is one of the best parts of the language.
    - ls612 2y ago
      I hate with a burning passion having to manage packages myself. MATLAB comes preinstalled with everything and the kitchen sink.
      - adgjlsfhk1 2y ago
        Fair enough. It probably would make sense to have a Conda like release of Julia that comes out every year with a broad but curated selection of packages.
        stillyslalom 2y ago
        Long ago, I trawled through Matlab's docs to come up with a set of Julia packages matching what's build into Matlab: https://discourse.julialang.org/t/julia-for-matlab-users-club/12872/8 https://discourse.julialang.org/t/julia-for-matlab-users-clu... I don't think you'd actually want to include each of those packages in a standard distro: does the average user really need to programmatically send emails or deal with Voronoi tessellations? Probably not, but I still think there's value in a batteries-included approach, especially when working with students.

cactusfrog 2y ago

I think it has correctness issues

bdjsiqoocwk 2y ago
[flagged]
maximilianroos 2y ago
Source?
- minetest2048 2y ago
  https://yuri.is/not-julia/ https://yuri.is/not-julia/ HN discussion: https://news.ycombinator.com/item?id=31396861 https://news.ycombinator.com/item?id=31396861
  - ben_sisko 2y ago
    This link lays out the case in an exceptionally thorough and damning (for Julia) way.
  - cpfiffer 2y ago
    I think my general sense of this article is that all of these have been fixed. The language is relatively new, and the core devs are responsive. Using anything new comes with risks. I think the community appreciated a detailed and generally well-reasoned diagnosis, but at the same time these things are relatively easily addressed.

jarbus 2y ago

I use Julia regularly for experimental machine learning. It’s great for writing high performance, distributed code and even easier than Python for this kind of work, since I can optimize the entire stack in a single language. Not sure if it’s growing in popularity but it’s really solid for what it does

nextos 2y ago
Me too, and I'd like it to become mainstream. The major problem right now is that it doesn't have anything that is close to Torch or JAX in performance and robustness. Flux et al. are 90% there, but the last 10% requires a massive investment, and Julia doesn't have any corporate juggernaut funding development like Meta or Google. This is hurting Julia's adoption. The rest of the language is incredibly elegant, as there is no 2-language divide like in Python. Furthermore, it is really performant. With very little effort one can write code that is within 1.5-2x of C++, often closer. One possibility is that something like Mojo takes Julia's spot. Mojo has some of the advantages of Julia, plus very tight integration with Python, its syntax and its ecosystem. I would still prefer Julia, but this is something to keep in mind.
- anonylizard 2y ago
  LLMs massively compound the advantage of existing popular languages, namely python. Any new learner will find it infinitely easier to use sonnet 3.5 to overcome the so called '2 language barrier' for python, while the lacking data for Julia becomes the real barrier. This issue will remain until LLMs get so smart they can maybe self-iterate and train on a given language. By then though, we'd likely get languages designed and optimized for LLMs.
  - SatvikBeri 2y ago
    For what it's worth I've found Claude Sonnet to work really well with Julia. One fun exercise was when a friend handed me a stack of well-written, very readable Python code that they were actually using. They were considering rewriting it in C, which would have been worth it if they could get a 10x speedup. I had Sonnet translate it to Julia, and it literally ran 200x faster, with almost identical syntax.

deleted 2y ago

[deleted]

currymj 2y ago

certainly yes in scientific computing, less so in ML/data science. there's much of the culture of scientific computing in economics -- lot of heavy numerical stuff in addition to the statistical modeling you might expect.

tkuraku 2y ago

I want to really like Julia. For me it felt like more work than python for simple stuff and not that much less work than c++ if you are trying to get the best performance. It is a cool language though.

SatvikBeri 2y ago

It's reasonably popular, growth has continued at a slow but steady pace. It's never going to become Python or anything but it's great in its niche. We use Julia in our hedge fund, it allows our researchers to write Python-like syntax while being very easy to optimize – compared to numpy code we've had a relatively easy time getting Julia to run 20x-1000x faster depending on the module, which has resulted in a very large reduction in AWS bills.