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Julia Computing raises $24M Series A

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Re: Julia Computing raises $24M Series A

#81

Post by ampdepolymerase »

krastanov wrote: post #46
awaythrowact wrote: post #39

Congrats to the Julia team. I am a python developer who has dabbled with Julia but it never stuck for me. I think Julia was built by academics for other academics running innovative high performance computing tasks. It excels at the intersection of 1) big data, so speed is important, and 2) innovative code, so you can't just use someone else's C package. Indeed, Julia's biggest successful applications outside academi…

There is another important niche I am particularly excited about: programming language research geeks and lisp geeks. The pervasive multiple-dispatch in Julia provides such a beautiful way to architecture a complicated piece of code.

Those two niches don't pay. They are effectively useless outside of the occasional evangelism on HN.

Re: Julia Computing raises $24M Series A

#82

Post by pjmlp »

krastanov wrote: post #46
awaythrowact wrote: post #39

Congrats to the Julia team. I am a python developer who has dabbled with Julia but it never stuck for me. I think Julia was built by academics for other academics running innovative high performance computing tasks. It excels at the intersection of 1) big data, so speed is important, and 2) innovative code, so you can't just use someone else's C package. Indeed, Julia's biggest successful applications outside academi…

There is another important niche I am particularly excited about: programming language research geeks and lisp geeks. The pervasive multiple-dispatch in Julia provides such a beautiful way to architecture a complicated piece of code.

Yep, that is my niche.

Re: Julia Computing raises $24M Series A

#83

Post by tylermw »

krastanov wrote: post #50
notafraudster wrote: post #30

Earlier quoted context omitted.

What's the argument against using R and dropping into RCpp for very limited tasks? I (helped) write a very widely used R modelling package and while I wasn't doing anything on the numerical side, we seemed to get great performance from this approach -- and workflow-wise it wasn't too dissimilar to 25 years ago where I had to occasionally drop in X86 assembly to speed up C code! (Not a hater of Julia at all, very much…

Interoperability between libraries that expect your code to be pure R / pure Python. If you use RCpp or Cython or CPython then you lose much of the magic behind the language that enables the cool (but frequently slow) features. My biggest pain point in this situation: you can not use SciPy or Pillow or Cython code with Jax/Pytorch/Tensorflow (except in very limited fashion). Differential equation solvers that need to…

Once your Rcpp code is compiled, it's almost indistinguishable from base R (when you're calling it). All R functions eventually end up calling R primitives written in C, and Rcpp just simplifies the process of writing and linking C/C++ code into the R interpreter.

The only difficulty with Rcpp-based R packages is you have to ensure the target system can compile the code, which means having a suitable compiler available.

Re: Julia Computing raises $24M Series A

#84

Post by pjmlp »

ampdepolymerase wrote: post #81
krastanov wrote: post #46

Earlier quoted context omitted.

There is another important niche I am particularly excited about: programming language research geeks and lisp geeks. The pervasive multiple-dispatch in Julia provides such a beautiful way to architecture a complicated piece of code.

Those two niches don't pay. They are effectively useless outside of the occasional evangelism on HN.

Who do you think puts money to action on LLVM, GCC, Swift, Rust, .NET (C#, F#, VB), Scala,....?

Re: Julia Computing raises $24M Series A

#85

Post by vletal »

krastanov wrote: post #46
awaythrowact wrote: post #39

Congrats to the Julia team. I am a python developer who has dabbled with Julia but it never stuck for me. I think Julia was built by academics for other academics running innovative high performance computing tasks. It excels at the intersection of 1) big data, so speed is important, and 2) innovative code, so you can't just use someone else's C package. Indeed, Julia's biggest successful applications outside academi…

There is another important niche I am particularly excited about: programming language research geeks and lisp geeks. The pervasive multiple-dispatch in Julia provides such a beautiful way to architecture a complicated piece of code.

> The pervasive multiple-dispatch in Julia provides such a beautiful way to architecture a complicated piece of code.

On the other hand it makes function discovery almost impossible [1]. Combined with the way how 'using' keyword exports a predefined subset of functions, this makes the language doomed from larger adoption outside of academia at least as long as there is no superb autocompletion and IDE support.

[1] https://discourse.julialang.org/t/my-mental-load-using-julia...

Re: Julia Computing raises $24M Series A

#86

Post by amkkma »

systemvoltage wrote: post #68
awaythrowact wrote: post #39

Congrats to the Julia team. I am a python developer who has dabbled with Julia but it never stuck for me. I think Julia was built by academics for other academics running innovative high performance computing tasks. It excels at the intersection of 1) big data, so speed is important, and 2) innovative code, so you can't just use someone else's C package. Indeed, Julia's biggest successful applications outside academi…

Great summary. I've worked with scientists that love Julia and more power to them. As a software engineer, there are still rough edges in productionizing Julia (yes, I know there are a few examples of large scale production code). As soon as you take Julia out of notebooks and try to build moderately complex apps with it, you realize how much you miss Python. Having used Julia for last 4 years and having to maintain…

> As soon as you take Julia out of notebooks and try to build moderately complex apps with it, you realize how much you miss Python

Why's that? What features or lack thereof of Julia contribute to that experience?

Re: Julia Computing raises $24M Series A

#87

Post by mccoyb »

vletal wrote: post #85
krastanov wrote: post #46

Earlier quoted context omitted.

There is another important niche I am particularly excited about: programming language research geeks and lisp geeks. The pervasive multiple-dispatch in Julia provides such a beautiful way to architecture a complicated piece of code.

> The pervasive multiple-dispatch in Julia provides such a beautiful way to architecture a complicated piece of code. On the other hand it makes function discovery almost impossible [1]. Combined with the way how 'using' keyword exports a predefined subset of functions, this makes the language doomed from larger adoption outside of academia at least as long as there is no superb autocompletion and IDE support. [1] ht…

Have you seen Shuhei Kadowaki's work on JET.jl (?)

If you're curious: https://github.com/aviatesk/JET.jl

This may seem more about performance (than IDE development) but Shuhei is one of the driving contributors behind developing the capabilities to use compiler capabilities for IDE integration -- and indeed JET.jl contains the kernel of a number of these capabilities.

Re: Julia Computing raises $24M Series A

#88

Post by trenchgun »

ampdepolymerase wrote: post #81
krastanov wrote: post #46

Earlier quoted context omitted.

There is another important niche I am particularly excited about: programming language research geeks and lisp geeks. The pervasive multiple-dispatch in Julia provides such a beautiful way to architecture a complicated piece of code.

Those two niches don't pay. They are effectively useless outside of the occasional evangelism on HN.

Money does not create code, humans do.

Re: Julia Computing raises $24M Series A

#89

Post by Duller-Finite »

mr_overalls wrote: post #60

Julia seems like such a superior language compared to R. What would be required for it to supplant R for statistical work (or some subset of it)?

As an example, Douglas Bates, the author of R's lme4 excellent package for generalized linear mixed-effects models, has switched to julia to develop MixedModels.jl. The julia version is already excellent, and has many improvements over lme4.

Re: Julia Computing raises $24M Series A

#90

Post by amkkma »

systemvoltage wrote: post #77
lycopodiopsida wrote: post #66

Earlier quoted context omitted.

“Only” to write a very high amount of high-quality statistical and plot packages…

And clone/bribe Hadley Wickham :-) He is a tour de force of R.

It's already superior to R for data munging stuff, imo

https://twitter.com/evalparse/status/1416039770833096706

And https://github.com/JuliaPlots/AlgebraOfGraphics.jl >>> GoG

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