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How an MIT research project became the Julia programming language

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Re: How an MIT research project became the Julia programming language

#21

Post by jhoechtl »

muragekibicho wrote: post #4

Julia uses 1-based indexing. It's competes with R and Matlab for the same set of users. Both R and Julia have their core functions written in C++. Absolutely nothing new. From my experience, grad students use Julia when their PI thinks a new programming language will help differentiate their next NSF proposal among vast funding requests.

> Julia uses 1-based indexing.

That rules it out to become a successor to Python. It sounds reasonable until you start interacting with other libraries.

I do know the attemp to justify it for Lua and I don't buy it.

Re: How an MIT research project became the Julia programming language

#22

Post by skew-aberration »

tonyarkles wrote: post #16
skew-aberration wrote: post #15

Earlier quoted context omitted.

It kinda feels like Julia competes for the people who write the libraries for R and Matlab. Writing fast and elegant ODE solvers, etc in Julia seems to be easier than the others and they've attracted a lot of academics for that reason.

I'm currently split between Python and Julia, having used R happily in the past for data analysis and Matlab for this and that in my EE program. For me, Julia crushes one niche that the rest of them are not good at: making the math look like the math. https://docs.sciml.ai/ModelingToolkit/stable/tutorials/nonli... If you've used something like SciPy or symbolic Matlab or Maxima or whatever, it always feels like I'm v…

I definitely agree. And the common performance optimization metaprogramming (like 'do it this way for this type of input') works so much better with multiple dispatch, tag structs. Way ahead of C++ expression templates and much more pleasant than macros, concepts, etc.

Some of the lower-level APIs like those for concurrency were quite poorly thought out though, at least when I last used Julia. Condition variables don't have equivalent of pthread_timed_wait. Condition variables and channels APIs are not well integrated, design wise. I found so many such issues that it convinced me Julia wasn't general purpose enough. It felt like the features were a bit half-baked and had been hacked together by someone who knew their value but lacked the deep experience/knowledge to pull them all together into a single cohesive vision. Same issues as python, POSIX, etc.

Re: How an MIT research project became the Julia programming language

#23

Post by Joel_Mckay »

hn_submit wrote: post #18

Safe for BASIC I believe it's very difficult to make a programming language which is easy-to-learn like the creators of Julia proclaim. It may be somewhat easier to implement specific scientific functionality in the language but does that warrant the creation of yet another language? Scanning the language it doesn't strike me at all as "simple."

Julia abstracts entire programming paradigms in single characters.

https://juliahep.github.io/Hands-on-Julia-for-particle-physi...

BASIC "made simple things easy, and hard things impossible..."

Julia is the first language I've seen in years that implicitly abstracts parallelism cleanly. =3

Re: How an MIT research project became the Julia programming language

#24

Post by prof-dr-ir »

Julia is fantastic if you do numerical work and want to write out your inner loops explicitly without sacrificing too much performance, either for pedagogical reasons or because you want to fiddle with the algorithm.

It is a lot of fun to start from an empty file, add maybe an import LinearAlgebra, and develop things like a convolutional neural network or a Markov-chain Monte Carlo algorithm completely from scratch. And then it is very rewarding to have such a program be fast enough (looking at Python here) to train on the MNIST dataset or find reasonable estimates for critical exponents.

I am not sure there are languages better suited than Julia for these kind of things.

Re: How an MIT research project became the Julia programming language

#25

Post by tonyarkles »

skew-aberration wrote: post #22
tonyarkles wrote: post #16

Earlier quoted context omitted.

I'm currently split between Python and Julia, having used R happily in the past for data analysis and Matlab for this and that in my EE program. For me, Julia crushes one niche that the rest of them are not good at: making the math look like the math. https://docs.sciml.ai/ModelingToolkit/stable/tutorials/nonli... If you've used something like SciPy or symbolic Matlab or Maxima or whatever, it always feels like I'm v…

I definitely agree. And the common performance optimization metaprogramming (like 'do it this way for this type of input') works so much better with multiple dispatch, tag structs. Way ahead of C++ expression templates and much more pleasant than macros, concepts, etc. Some of the lower-level APIs like those for concurrency were quite poorly thought out though, at least when I last used Julia. Condition variables don…

Yeah, the nifty part is instead of trying to write your whole multi-threaded high performance tool in Julia, there is excellent support for taking the math work you’ve done and codegen C out of it. Am very happily using that in prod today for a thing and it works awesome.

Re: How an MIT research project became the Julia programming language

#26

Post by Joel_Mckay »

tonyarkles wrote: post #16
skew-aberration wrote: post #15

Earlier quoted context omitted.

It kinda feels like Julia competes for the people who write the libraries for R and Matlab. Writing fast and elegant ODE solvers, etc in Julia seems to be easier than the others and they've attracted a lot of academics for that reason.

I'm currently split between Python and Julia, having used R happily in the past for data analysis and Matlab for this and that in my EE program. For me, Julia crushes one niche that the rest of them are not good at: making the math look like the math. https://docs.sciml.ai/ModelingToolkit/stable/tutorials/nonli... If you've used something like SciPy or symbolic Matlab or Maxima or whatever, it always feels like I'm v…

Julia is fun, but is still mostly an academic language. Very few shops will use it in the private sector. Python is also more common as a prototype integration language, and rarely seen in industrial areas.

If you are an EE that wants to remain employed... than make sure you have documented hours with C/C++, Verilog on Zynq, and ladder logic for Rockwell automation products.

Best of luck =3

Re: How an MIT research project became the Julia programming language

#27

Post by ssivark »

maxall4 wrote: post #2

I really want to like Julia. It has a nice type system, a good ecosystem, reasonable syntax, and it’s far faster than Python. But there are several issues with its DX that prevent me from using it: the most severe of which being the complete lack of a cache for the JIT (or JIT like system), inducing multi second compile times for scripts that run in (I last tried Julia a few years ago; perhaps this has been improved…

I imagine this will only become more important as we have more agents working on codebases in parallel; so a clean solution would be extremely valuable.

On the flip side, the rise of agentic coding means that the library/batteries mismatch against python will largely stop mattering. It should be very easy to have an agent implement large libraries -- especially if they can be just a translation from one language to another (esp. one with with better primitives!)

Re: How an MIT research project became the Julia programming language

#28

Post by kvchiu »

jhoechtl wrote: post #21
muragekibicho wrote: post #4

Julia uses 1-based indexing. It's competes with R and Matlab for the same set of users. Both R and Julia have their core functions written in C++. Absolutely nothing new. From my experience, grad students use Julia when their PI thinks a new programming language will help differentiate their next NSF proposal among vast funding requests.

> Julia uses 1-based indexing. That rules it out to become a successor to Python. It sounds reasonable until you start interacting with other libraries. I do know the attemp to justify it for Lua and I don't buy it.

[dead]

Re: How an MIT research project became the Julia programming language

#29

Post by myrmidon »

Julia is really fun to use, and I think they are onto something with the type system: Complex types interact much better across module boundaries than in most other languages; here's a good talk on this by one of the creators:

https://www.youtube.com/watch?v=kc9HwsxE1OY

The biggest drawback of the language in my view is that Julia is an LLVM toolchain "pretending" to be an interpreted language (it is, technically), this often leaks as very slow first execution latency (and almost forces you to keep the interpreter open instead of just calling it on the file you're editing).

Re: How an MIT research project became the Julia programming language

#30

Post by radiator »

Do you think the criticism in the article "Why I no longer recommend Julia" about correctness is still valid? * there are too many correctness and composability bugs throughout the ecosystem to justify using it in just about any context where correctness matters *

https://yuri.is/not-julia/

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