Did #julialang end up kinda stalling or at least plateau-ing lower than hoped?
Did #julialang end up kinda stalling or at least plateau-ing lower than hoped?
Did #julialang end up kinda stalling or at least plateau-ing lower than hoped?
I know it's got its community and dedicated users and has continued development.
But without being in that space, and speculating now at a distance, it seems it might be an interesting case study in a tech/lang that just didn't have landing spot it could arrive at in time as the tech-world & "data science" reshuffled while julia tried to grow ... ?
Can a language ever solve a "two language" problem?
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@maegul @programming Maybe nobody (save for the Julia developers) ever cared about the "two language problem". I see folks are just happy writing high performance tools in Rust with Python wrappers.
In any case, I'm happy that the Julia folks gave birth to things like DifferentialEquations.jl, truly a piece of art. Anything that helps scientists and engineers move away from MATLAB is welcome.
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@tschenkel @astrojuanlu @programming
I'd suppose part of the problem might be that there's a somewhat hidden 3rd category of user that "feels" whatever added complexity there is in a two-language lang like julialang and has no real need for performant "product" code.
And that lack of adoption amongst this cohort and your first enforces lang separation.
I may be off base with whether there's a usability trade off, but I'd bet there's at least the perception of one.
@maegul @astrojuanlu @programming
You put your finger right on it. If I tell people that they can replace Matlab or Python with Julia, then there is often not the pressure justifying learning another language.
But when I show them, that (a) the fundamental syntax is nearly the same as Matlab or (b) that the same thing in Python is much more clunky (Numpy and Scipy are not exactly elegant in my eyes), and they can get 40 and 600 times speedup respectively, then there is much more interest.
I needed the performance and DifferentialEquations.jl gave it, and my students stay for the easier language. But then they don't have a long time with Matlab or Python under their belts, so the perceived pain of learning a new language to the same level is less, of course.
At the moment we still teach Matlab mostly, because that's what employers expect, but some of my students choose Julia in addition to it. And they do like it.
More and more universities move their scientific "computational thinking" type of classes to Julia (from either Matlab or Python), which may move the trend when their will be more STEM graduates knowing the language.
Considering, it may be worth highlighting that tools like Jax exist as well (https://github.com/google/jax). These have even become an expected integration in some toolkits (e.g., numpyro)
It may not be the most elegant approach, but there's a lot of power in something that "mostly just works and then we can optimize narrowly once we find a problem"
It doesn't make a solution that solves this mess bad, but I do wonder about it being a narrow niche
@hrefna @tschenkel @astrojuanlu @programming
Yea ... it seems that things like this are part of Julia's problem ...
that for many the "two language problem" is actually the "two language solution" that's working just fine and as intended, or as you say, well enough to make an ecosystem jump seem too costly.
@hrefna @maegul @astrojuanlu @programming
Interesting. But seems to be limited to arrays. And I'm wondering what the benefit is over regular Numpy. In my tests I did not find Numpy to be slower than any other Blas or Lapack implementation (if type stable and immutable).
Do you have any comparisons?
In any case, I don't think it would work for solving ODEs unless I'd implement the RHS in a JIT compiled function.
I used to love Python, but now it looks like a collection of add-ons/hacks to fix the issues I have.
Mostly its advantage as far as arrays go is its ability to push things out to an accelerator (GPU) without making code changes. Also its JIT functionality is a good bit faster than using pytorch's (at least anecdotally).
My experience with it is not at all related to ODEs (more things like MCMC) and I have no direct experience with its gradient functionality and only limited with its auto vectorization, so take my experience with a grain of salt.
@maegul @astrojuanlu @programming
BTW, I'm not talking about "product code" in Julia, necessarily. The point is that I can stay at the "research code" level and don't have to rewrite anything, but can just move from a toy problem with a few parameters to a full size mod with 100s of parameters.
In my case I had a Python code solving an ODE system and a global optimisation of all parameters took a week on the 24 cores on a cluster. The same case runs over the lunch break on my laptop in Julia.
For the most recent paper a PhD student of ours use 2000-ish GPU hours for a global sensitivity/uncertainty analysis. That would have been impossible in Python or Matlab. On a single GPU that would have taken 10 years (300 years in Python), instead of 3 months*.
*yes, you can just through more GPUs at the problem, but then we can start the discussion about CO2 footprint being 40 or 600 times higher, respectively.
Note: all our benchmarks are for solving ODE system. Just doing linear algebra, there isn't really a difference in speed between Matlab, Bumpy, or Julia, because that's all going to run on the same LA libraries.
@tschenkel @astrojuanlu @programming
I understood ... I was reaching for some shorthand (500 char limits FTW!)
There's probably a good amount of work that exists somewhere between your needs and "could be a spreadsheet", where caring about performance isn't an issue or hasn't surfaced yet, either practically or culturally (where the boundaries of what research *can* be done "tomorrow" are of importance)
BTW, cheers for all the info!!
@tschenkel @astrojuanlu @maegul @programming I completly agree with your second point.
Anything that helps scientists and engineers move away from MATLAB is welcome.
The MATLAB language may be pretty bad but IMO that's not what makes MATLAB good. Rather it's:
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Every signal processing / maths function is available and well documented. I don't know how well Julia does on this but I know I wouldn't want to use Python for the kinds of things I used MATLAB for (medical imaging). You don't have to faff with pip to get a hilbert transform or whatever...
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The plotting functionality is top notch. You can easily plot millions of points and it's fast and responsive. Loads of plotting options. I just haven't found anything that comes close. Every other option I've tried (a lot) only works for small datasets.
@FizzyOrange The problem with Python for signal processing and stuff like that is not pip (I know it's a meme by now, but it's really not that bad) but rather, the fact that some SciPy subpackages are barely maintained
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> Maybe nobody (save for the Julia developers) ever cared about the "two language problem"
Yea, it’s what prompted my post. I saw in a rust forum push back on the two language thing but from the lower level side (where they were arguing about introducing lazier memory management facilities on the basis that you should just use swift/Python etc).
And re MATLAB … absolutely! This is not a diss against Julia at all.