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Some Insights from a Julia Developer

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Re: Some Insights from a Julia Developer

#51

Post by rattray »

wallnuss wrote: post #30
88e282102ae2e5b wrote: post #17

It's great and all, but I can't justify switching languages for minor improvements over Python + numpy/scipy. I'd be abandoning: * My deep knowledge and experience with Python * My entire codebase * The ability to work on projects with colleagues who don't also switch * The certainty that when I leave my current job, someone will be able to pick up after me * Zero-based indexing I've started to do some work in Rust w…

* My entire codebase Julia has great interoperability between Python (checkout PyCall.jl) and many other languages. You don't need to abandon your old codebase. With regards to your other points, a lot of that is subjective and hard to argue about, but for me Julia has drastically changed the way I approach writing scientific code and is not just an incremental improvement upon the status quo.

Interesting. How has it changed your approach?

Re: Some Insights from a Julia Developer

#52

Post by vvanders »

jernfrost wrote: post #33
88e282102ae2e5b wrote: post #17

It's great and all, but I can't justify switching languages for minor improvements over Python + numpy/scipy. I'd be abandoning: * My deep knowledge and experience with Python * My entire codebase * The ability to work on projects with colleagues who don't also switch * The certainty that when I leave my current job, someone will be able to pick up after me * Zero-based indexing I've started to do some work in Rust w…

The improvements are not minor they are massive. Citing rust shows that the advantage of Julia has not been explained well enough. Julia allows you to write as performant code as Rust with a much smaller investment in learning. You cite your concern for spending time learning something new. That makes no sense considering the high learning curve and complexity of Rust compared to Julia. Julia is quite fast to learn a…

Have a citation on the performance vs Rust?

I'm skeptical any GC'd language can approach C/Rust unless they have explicit mechanisms to do data layout for using the cache/prefetcher to the fullest degree.

Re: Some Insights from a Julia Developer

#53

Post by rattray »

j7ake wrote: post #36
mkborregaard wrote: post #32

Earlier quoted context omitted.

What makes you say it lacks critical mass? I don't think that's accurate, but of course it takes time. Implementation time is important, yes - but julia is not just a fast-to-run-language, which is essentially the point - it is a fast-to-implement language.

At least in the field of computational biology most people use R or python because of the wealth of statistical packages and biology-specific packages (biomaRt) that makes implementing and testing models on biological data much faster than in Julia. It's always this problem, if everybody is using R, it is hard to migrate to Julia. But in order to migrate to Julia you need people to stop using R and switch to Julia.

Is there no Julia wrapper around biomaRt? Would it be challenging to create and maintain?

(I don't mean to imply that _you_ should be the one to do it - I'm genuinely curious)

Re: Some Insights from a Julia Developer

#55

Post by j7ake »

I looked at your blog post on solving differential equations and it looks pretty attractive. I will install Julia and play around with this differential equation solver. At the moment R and python are awkward with differential equations, and I don't want to be married to matlab.

Re: Some Insights from a Julia Developer

#56

Post by sin7 »

noobhacker wrote: post #47
jampekka wrote: post #37

Earlier quoted context omitted.

I mostly use Python, although it has some syntax issues too (eg. no real lambdas, I'd prefer no parenthesis for function calls etc). I don't see why that's a numpy problem. If you just do "from numpy import *" you can pollute your namespace with hundreds of symbols, just like eg. MATLAB. R not just looks ugly, but is quite horrible in the inside too. For example the variable scoping is one of the most insane I have e…

Could you clarify what's insane about the variable scoping in R? I'm a bit too close to R so I'm afraid I'm oblivious.

I use a lot of R. The thing that drives me insane is when you create a function such as multxy <- function (x){x * y}, then you call multxy(6) R will not return an error as long as y is in the parent environment. That's pretty insane.

Re: Some Insights from a Julia Developer

#57

Post by Bromskloss »

pvg wrote: post #38
disconnected wrote: post #23

Earlier quoted context omitted.

I don't have any particular feelings toward one or the other (it is a convention, get over it), but I think that zero-based indexing is just an artifact of C that stuck around. In C, the array syntax is "mostly" just syntactic sugar for pointer arithmetic. When you do "a[n]=value;" this is equivalent to " *(a+n) = value;". To get the nth cell of an array, you just add "n" to your base pointer "a". Array indexing, the…

Zero based indexing is not a C artifact. Here's Dijkstra writing about it in '82: https://www.cs.utexas.edu/users/EWD/transcriptions/EWD08xx/E...

Note how, in the PDF version [0], Dijkstra numbers the pages starting on zero (handwriting, upper right corner), but whoever created the PDF disregarded its message and did numbering starting on one (lower right corner). :-)

[0] https://www.cs.utexas.edu/users/EWD/ewd08xx/EWD831.PDF

Edit: Fixed link. Hacker News apparently doesn't understand the delimiting of a URL with "". >-( https://tools.ietf.org/html/rfc3986#appendix-C

Re: Some Insights from a Julia Developer

#58

Post by faitswulff »

vvanders wrote: post #52
jernfrost wrote: post #33

Earlier quoted context omitted.

The improvements are not minor they are massive. Citing rust shows that the advantage of Julia has not been explained well enough. Julia allows you to write as performant code as Rust with a much smaller investment in learning. You cite your concern for spending time learning something new. That makes no sense considering the high learning curve and complexity of Rust compared to Julia. Julia is quite fast to learn a…

Have a citation on the performance vs Rust? I'm skeptical any GC'd language can approach C/Rust unless they have explicit mechanisms to do data layout for using the cache/prefetcher to the fullest degree.

Julia's website has some benchmarks: https://julialang.org/benchmarks/

C is the leftmost dot, Julia is just to the right.

Re: Some Insights from a Julia Developer

#59

Post by williamstein »

ChrisRackauckas wrote: post #11
williamstein wrote: post #5

Strangely, the only mention of Cython is to point at that we have had less developers than Julia: "as evidenced by the over 500 committers to just the Base language, more than projects like Cython has ever had!"

I don't find it strange: I wrote this to say why I like using Julia and point out what the community is missing, not as a comparison to every other JIT in existence. But if you want to know why I gave up on Cython, I'll lay it out for you. I tried it almost 2 years ago because some documents in a course had IPython notebooks which used it. So I did some standard scientific computing stuff like write some Runge-Kutta…

> I couldn't find a page which explained how to do high precision arithmetic in Cython or Numba, so I completely gave up.

When that happened to me, I wrote Sage (http://sagemath.org).

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