Comment by datanecdote | Hacker News Reader

Comment by datanecdote | Hacker News Reader

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Let me second GP’s sentiment. I find Julia really slow for my purposes. I don’t know his reasoning, but I will explain mine. None of this is surprising and is oft discussed.

Julia (at least by default) is constantly recompiling everything. This is a huge pain in a REPL style setup where you want to tweak one thing and see the changes, again and again. I know the Julia ecosystem is working on better caching etc to fix this problem but it’s a problem.

Also, despite the marketing claims around the language, expertly crafted C usually beats Julia in performance. So if your “python” program is spending most of its time in Numpy/PyTorch/etc, it will beat Julia, unless you’re writing a fancy “put a differential equation in a neural network in a Bayesian Monte Carlo” program that benefits from cross compiling across specialized libraries.

Finally, the Julia libraries are just not as mature as python’s. Armies of developers and larger armies of users have battle tested and perfected python’s crown jewel libraries over many years. Often when someone posts a bad benchmark to the Julia forums they can “fix” it in the library implementation, proving the correctness of the theoretical case for Julia. But in reality many such problems remain to be fixed.

Julia is really cool and does have many inherent advantages over python. But it’s not the silver bullet many of its proponents suggest it to be. At least not yet. Every few years I check out Julia and I hope one day it does become that perfect language. I think it will. I just fear it will take longer than many others hope.

I appreciate your well-balanced critique, thanks.

> Julia (at least by default) is constantly recompiling everything. This is a huge pain in a REPL style setup where you want to tweak one thing and see the changes, again and again. I know the Julia ecosystem is working on better caching etc to fix this problem but it’s a problem.

Maybe try Revise.jl? There are a few changes it can't handle, but you can do a lot of development without ever restarting. (Disclaimer: I'm its main author.)

> expertly crafted C usually beats Julia in performance

This isn't generically true, and there are now quite a few examples of the converse. I linked to it above as well, but check out the benchmarks in LoopVectorization's documentation (https://chriselrod.github.io/LoopVectorization.jl/latest/exa...) for examples of beating MKL, one of the most carefully-engineered libraries in existence.

I think an exciting area of growth for Julia will be exploiting the fact that Julia's compiler, written mostly in Julia, is more "morphable" than most and may develop its own plug-in architecture. This seems likely to provide performance opportunities that many fields seem hungry for.

> the Julia libraries are just not as mature as python’s

On balance I agree. While there are already many examples where Julia makes things easier than Python, as of today there are many more examples to the contrary. Julia's libraries are advancing rapidly, but I expect it will take a few more years of development until it's no longer so one-sided.

My thoughts exactly.

I would just add, I feel Python is stagnating as a scientific programming _language_. The libraries, ecosystem etc are still great, and Python is still a great language, but these days the development focus seems to be on type hints and unicode support.

I wouldn’t be surprised if Julia takes over, simply because it actually focuses on scientific programming. To me, personally, that would be a shame; good for Julia, but I still find Python a better language overall.

Preach, brother.

I’m cautiously optimistic that JAX (or something like JAX) can save the python programming language from stagnation by essentially building a feature-complete reimplementation of the language with JIT and autograd baked into the core. I’m praying that Google diverts like 10% of TF’s budget to JAX.

That way I don’t have to learn to love a bunch of unnecessary semi colons and “end”s littering up my beautiful zero-indexed code ;-)

Julia code almost never has semicolons. Semicolons can be used at the REPL to suppress printing, but actual Julia code does not normally use semicolons.

I personally like the "end"s because I like the symmetry and they're prettier than curly braces. Also, there are some syntax color themes that color the "end"s in a darker color in order to de-emphasize the "end"s, which can be nice depending on your taste.

That was mostly meant as a joke, thus the “;-)”

I don’t really care much about syntax choices, but my small complaint about “end” is that it takes up a line which reduces the amount of business-logic code I can fit on one screen, especially if you ever get into lots of nested loops and conditionals.

To each their own, but I usually try to refactor when I hit too many nested loops or branched statements - it's usually a sign of missing some abstraction or trying to be too clever.

@Sukera

Fair, but, if I break up all the loops and if statements into functions, those functions still have “end”s

For the record, I'm a fan of both Python and Julia (though, I believe that the latter is not yet ready for general programming mainstream industrial use). As for Python "stagnating as a scientific programming _language_", it is totally understandable. It tries to be the language of choice for everyone. However, as we know, "you can please some of the people all of the time, you can please all of the people some of the time, but you can’t please all of the people all of the time".

My point is, Python is no longer _trying_ to please scientific programming circles. It is developing, just not in these directions.

Understood, fair enough.

> but these days the development focus seems to be on type hints and unicode support.

It's a result of the language maturing. The tradeoff is between groundbreaking innovation and being a stable language with a large user base. You can't have it both ways.

There are features Python is missing as a scientific language, that are not a focus. Proper multicore support, GIL, any static safety (which type hints sort of address but not really).

It was fine in Python 2, it was cheap and cheerful. It feels likes Python 3 is running out of ideas for improvement, and yet these are not at all in the scope of work.

Cf Julia that treats all these features as first class problems.

I am wondering if this is really a problem? I just parallelized some numerical code and instead of threads (Gil problem), I use processes. As far as I understand the only drawback would be that the parallel items cannot share memory, well do you do that? I find it hard to reason about correctness in these cases.

This works a bit. But sometimes sharing memory is useful. Sometimes you may want to parallelise a local function, or a callable class instance - except you can't.

And when I say, "it works", that's clearly on Linux and Mac. On Windows multiprocessing is very severely stunted by lack of forking.

Meanwhile, probably even my watch supports threads.

This might interest you: you can now turn off compiler optimizations at the module level, using a macro. For some people this speeds up the delopment cycle, as it skips most to the time-consuming compilation activity.

oh that is interesting. I'll give it a spin again this weekend. Thanks.

Caching has improves a ton in the last 3 minor releases.

Thanks. I watched the JuliaCon state of Julia presentation. As I wrote in my original post, I appreciate the investments the Julia core developers are making, that have improved but not eliminated this problem. I wish them luck.

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