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Julia and JuliaHub: Advancing Innovation and Growth

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Re: Julia and JuliaHub: Advancing Innovation and Growth

#21

Post by frakt0x90 »

jarbus wrote: post #19

I've used, and am still using, Julia for my PhD research. It's perfect for parallel/distributed computing, and the neural network primitives are more than enough for my purposes. Anything I write in pure julia runs really, really fast, and has great profiling tools to improve performance further. Julia also integrates with python, with stuff like PythonCall.jl. I've gotten everything to work so far, but it hasn't bee…

I agree on all points. I have used Python for 15 years, Julia for 3 and reach for Julia most of the time for personal projects. I was really stoked when at work the only FOSS solver for our problem was in Julia so we wrote the rest in it for easy integration. The only thing I dread is having to look for a new package since the ecosystem can be quite fragmented.

Re: Julia and JuliaHub: Advancing Innovation and Growth

#22

Post by Bostonian »

jarbus wrote: post #19

I've used, and am still using, Julia for my PhD research. It's perfect for parallel/distributed computing, and the neural network primitives are more than enough for my purposes. Anything I write in pure julia runs really, really fast, and has great profiling tools to improve performance further. Julia also integrates with python, with stuff like PythonCall.jl. I've gotten everything to work so far, but it hasn't bee…

Your last sentence applies equally to Fortran. How would you compare Julia and Fortran?

Re: Julia and JuliaHub: Advancing Innovation and Growth

#23

Post by tmvphil »

As someone working with it day to day, coming from around 18 years of mostly python, I wish I could say my experience has been great. I find myself constantly battling with the JIT and compilation and recompilation and waiting around all the time (sometimes 10 to 15 minutes for some large projects). Widespread macro usage makes stack traces much harder to read. Lack of formal interfaces means a lot of static checking is not practical. Pkg.jl is also not great, version compatibility is kind of tacked on and has odd behavior.

Obviously there are real bright spots too, with speed, multiple dispatch, a relatively flourishing ecosystem, but overall I wouldn't pick it up for something new if given the choice. I'd use Jax or C++ extensions for performance and settle on python for high level, despite its obvious warts.

Re: Julia and JuliaHub: Advancing Innovation and Growth

#24

Post by dv_dt »

NeutralForest wrote: post #5

I like the language but I can't help but feel it missed the train and that the ergonomics improvements it offers are too small to switch over from Python.

It does feel like julia will not make the leap to displace python, but for a long time python offered too few improvements over perl, so its not completely out of the question.

Re: Julia and JuliaHub: Advancing Innovation and Growth

#25

Post by 6gvONxR4sf7o »

I do scientific computing and a lisp was one of my first languages, so i feel like i ought to be the target audience, but it just never quite catches me.

It’s almost statically compilable which has almost gotten me to pick it up a few times, but apparently it still can’t compile a lot of the most important ecosystem packages yet.

The metaprogramming has almost gotten me to pick it up a few times, but apparently there aren’t mature static anti-footgun tools, even to the degree of mypy’s pseudo-static analysis, so I wouldn’t really want to use those in prod or even complex toy stuff.

It’s so damned interesting though. I hope it gets some of this eventually.

Re: Julia and JuliaHub: Advancing Innovation and Growth

#26

Post by realo »

Bostonian wrote: post #22
jarbus wrote: post #19

I've used, and am still using, Julia for my PhD research. It's perfect for parallel/distributed computing, and the neural network primitives are more than enough for my purposes. Anything I write in pure julia runs really, really fast, and has great profiling tools to improve performance further. Julia also integrates with python, with stuff like PythonCall.jl. I've gotten everything to work so far, but it hasn't bee…

Your last sentence applies equally to Fortran. How would you compare Julia and Fortran?

Julia uses LLVM for its jit architecture, if I recall correctly.

That makes it a good candidate for running well on ARM platforms (think embedded data processing at the edge).

Not sure how well fortran does on ARM.

Re: Julia and JuliaHub: Advancing Innovation and Growth

#28

Post by SatvikBeri »

Bostonian wrote: post #22
jarbus wrote: post #19

I've used, and am still using, Julia for my PhD research. It's perfect for parallel/distributed computing, and the neural network primitives are more than enough for my purposes. Anything I write in pure julia runs really, really fast, and has great profiling tools to improve performance further. Julia also integrates with python, with stuff like PythonCall.jl. I've gotten everything to work so far, but it hasn't bee…

Your last sentence applies equally to Fortran. How would you compare Julia and Fortran?

Julia is generally higher level than Fortran, with syntax inspired by Python/R/Matlab. We've been able to reliably hire Math PhDs and quickly get them productive in Julia, which would take much longer with Fortran.

Re: Julia and JuliaHub: Advancing Innovation and Growth

#29

Post by pjmlp »

realo wrote: post #26
Bostonian wrote: post #22

Earlier quoted context omitted.

Your last sentence applies equally to Fortran. How would you compare Julia and Fortran?

Julia uses LLVM for its jit architecture, if I recall correctly. That makes it a good candidate for running well on ARM platforms (think embedded data processing at the edge). Not sure how well fortran does on ARM.

Fortran does quite well on almost any major CPU since 1950's, including GPUs.

Actually one of the reasons CUDA won the hearts of researchers over OpenCL, is that Khronos never cared for Fortran, and even C++ was late to the party.

I attended one Khronos webminar where the panel was puzzled with a question from the audience regarding Fortran support roadmap.

NVidia is sponsoring the work on the LLVM Fortran frontend, so same applies.

https://flang.llvm.org/docs/

Re: Julia and JuliaHub: Advancing Innovation and Growth

#30

Post by cbruns »

I am a MATLAB and Python user who has flirted with julia as a replacement. I don't love the business model of JuliaHub, which feels very similar to Mathworks in that all the cool toolboxes are gated behind a 'contact sales' or high priced license. The free 20 hours of cloud usage is a non-starter. Also it seems that by default, all JuliaHub usage is default cloud-based? on-prem and airgapped (something I need) is implied to be $$$.

Open sourcing and maintaining some components of things like JuliaSim or JuliaSim Control might expand adoption of Julia for people like me. I will never be able to convince my company to pay for JuliaHub if their pricing is similar to Mathworks.

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