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Julia adoption keeps climbing

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Re: Julia adoption keeps climbing

#4

Post by superbcarrot »

Julia is a nice language, it's just tough to compete with Python.

- The beginner experience in Julia is still much worse than it is in Python. Stuff that should work intuitively sometimes doesn't, and when you get a cryptic error message, it's difficult to find relevant help online. And when you do find help, some of it is out of date because the language has changed over the past few years.

- You can squeeze a lot of performance out of Python and the ecosystem of libraries is hard to beat.

- Julia has to be way better than Python to give people an incentive to switch. Being just marginally better in some aspects of the language isn't enough. And it's very difficult to be much better than Python especially in useability and ecosystem.

Re: Julia adoption keeps climbing

#7

Post by ForHackernews »

superbcarrot wrote: post #4

Julia is a nice language, it's just tough to compete with Python. - The beginner experience in Julia is still much worse than it is in Python. Stuff that should work intuitively sometimes doesn't, and when you get a cryptic error message, it's difficult to find relevant help online. And when you do find help, some of it is out of date because the language has changed over the past few years. - You can squeeze a lot o…

You can call Python directly from Julia https://github.com/JuliaPy/PyCall.jl so much of the Python library ecosystem (say, matplotlib) is available to be used in Julia programs.

That helps the adoption story quite a bit. You can do the number-crunching in Julia where performance counts, and then analyse and present the results using Python.

Re: Julia adoption keeps climbing

#8

Post by cricci16 »

In my modest experience the perfect Julia slogan would be:

"fast as C, easy as python, but NEVER the two together"

All the sentences:

"When you’re writing various algorithms, you don’t necessarily want to think about whether you’re on a GPU, or whether you’re on a distributed computer. You don’t necessarily want to think about how you’ve implemented the specific data structure. What you want to do is talk about what you want to compute."

sound nice.

Except in practice, unless someone else bothered doing that for you, you have to do it yourself.

Re: Julia adoption keeps climbing

#9

Post by jarenmf »

I tested my well-optimised R code and saw only 3x to 10x performance gain. That's still not substantial enough currently to migrate a whole code base, in particular given that the libraries are also still not mature enough. The research group I'm working with also have no interest in adopting anything new, In fact most of our code is still in FORTRAN so that is something I would be more interested in migrating to Julia but I don't think that is happening anytime in the next 10 years

Re: Julia adoption keeps climbing

#10

Post by bsdubernerd »

the__alchemist wrote: post #2

Not until it addresses the JIT overhead.

In my tests I frequently switch between cpython, pypy and julia (depending on the libraries/task I want to perform) and I haven't found the JIT overhead to be worse than pypy on average.

Count me as one of the 1-based index haters, but I do love multiple dispatch and the language in general. As a language for explorative tools and analysis is on par of python (strict preference between the two according to taste).

To me the biggest flaw currently is the poor "catch" syntax for exception handling. There are countless spots where exceptions are incorrectly caught at random points due to the catch-all semantics hiding/masking/breaking stuff. This is one area where I really find the syntax has been chosen poorly and it's causing real damage.

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