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Julia 1.6 Highlights

julialang.org

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Re: Julia 1.6 Highlights

#2
The feature I'm most excited about is the parallel — and automatic — precompilation. Combined with the iterative latency improvements, Julia 1.6 has far fewer coffee breaks.

Re: Julia 1.6 Highlights

#6
On the package ecosystem side, 1.6 is required for JET.jl [0]. Despite being a dynamic language, the Julia compiler does a lot of static analysis (or "abstract interpretation" in Julia lingo). JET.jl exposes some of this to the user, opening a path for additional static analysis tools (or maybe even custom compilers).

[0]: https://github.com/aviatesk/JET.jl

Re: Julia 1.6 Highlights

#7
post #5

Cool, I was thinking of downloading the RC, the demo was so impressive. Will there be an M1 Mac version for 1.7?

I think so - Julia master branch (1.7 precursor) works on M1, but not all the dependencies that some packages require have been built for M1. Though, I understand that the wonderful packaging system and the folks who work on it are working on it.

> `git clone https://github.com/JuliaLang/julia` and `make` should be enough at this point.

https://github.com/JuliaLang/julia/issues/36617#issuecomment...

Re: Julia 1.6 Highlights

#8
post #3
post #2

The feature I'm most excited about is the parallel — and automatic — precompilation. Combined with the iterative latency improvements, Julia 1.6 has far fewer coffee breaks.

or sword fights https://xkcd.com/303/

Ohh, is that what the programmers were doing all through Halt and Catch Fire? Waiting for compilation? I couldn't understand how they got away with acting like naughty 5 year olds, throwing things at each other constantly.

Re: Julia 1.6 Highlights

#9
Is there a per-project way to manage dependencies yet? I find global package installation to be the biggest weakness of all the R projects out there. Anaconda can help, but it’s not widely used for R projects. And Docker... well, don’t get me started.

Re: Julia 1.6 Highlights

#10
post #9

Is there a per-project way to manage dependencies yet? I find global package installation to be the biggest weakness of all the R projects out there. Anaconda can help, but it’s not widely used for R projects. And Docker... well, don’t get me started.

Yes, absolutely. Julia has very strong per-project dependency tracking and reproducibility.
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