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Julia 1.6 Highlights

julialang.org

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Re: Julia 1.6 Highlights

#51
post #48

Earlier quoted context omitted.

I'm not a huge Julia user, but typically if they don't specifically mention they're segmenting runtime from compilation time with Julia, that's a bit of a red flag, because unlike Rust, Go, or C++ the compilation step isn't separate in Julia. To the user it just looks like it's running, when in reality it's compiling, then running, without really letting you know in between.

In the matrix multiplication example, the measurement is done via a simple t = time() results = calc(n) elapsed = time() - t So startup time at least isn't included. One might argue that this is still biased against Julia due to its compilation strategy, but fixing that would mean you'd have to figure out what the appropriate way to get 'equivalent' timings for any of the other languages would be as well - something…

If u cant even read code dont lie xD

    n = length(ARGS) > 0 ? parse(Int, ARGS[1]) : 100
    left = calc(101)  #  0.1
        println(stderr, "$(left) != $(right)")
        exit(1)
    end

    notify("Julia (no BLAS)\t$(getpid())")
    t = time()
    results = calc(n)
    elapsed = time() - t
    notify("stop")

Re: Julia 1.6 Highlights

#53
I like Julia (mostly because of multiple dispatch). The only thing that's lacking is an industry strength Garbage Collector, something that can be found in the JVM.

I know that you shouldn't produce garbage, but I happen to like immutable data structures and those work better with optimised GCs.

Re: Julia 1.6 Highlights

#54

Earlier quoted context omitted.

I’ve also looked for this, does it mean that I have to install julia on the target machine and it’ll recompile when running? Or are there steps to produce a binary (much like Go or C or Rust)??

Currently you can make a relocatable “bundle” / “app” with PackageCompiler.jl, but the bundle itself includes a Julia runtime. Making a nice small static binary is technically possible using an approach similar to what GPUCompiler.jl does, but the CPU equivalent of that isn’t quite ready for primetime.

You forgot to mention that a 'Hello World' standalone file is about 0.5 GB!

Re: Julia 1.6 Highlights

#56

I've been running the 1.6 release candidates, and the compilation speed improvements have been massive. There have been plenty of instances in the past where I've tried to 'quickly' show off some Julia code, and I end up waiting ~45 seconds for a plot to show or a minute for a Pluto notebook to run, and that's not to mention waiting for my imports to finish. It's still slower than Matlab for the first run, but it's a…

In terms of “don’t make me think about why Julia is fast but feels slow for casual use” this release is going to be a game changer.

I just did a “using Plots” in 1.6.0, and it was fast enough to not care about the delta between Plots and, say, R loading ggplot.

Huge kudos to the Julia team.

Re: Julia 1.6 Highlights

#57
post #53

I like Julia (mostly because of multiple dispatch). The only thing that's lacking is an industry strength Garbage Collector, something that can be found in the JVM. I know that you shouldn't produce garbage, but I happen to like immutable data structures and those work better with optimised GCs.

I didn't even know julia GC had issues. Care to elaborate?

Re: Julia 1.6 Highlights

#58

Earlier quoted context omitted.

They are measuring compile time, not runtime speed.

They are measuring compile time and runtime speed, not just runtime speed like for statically compiled langauges

Is that truly accurate though ? I could see them comparing say load time of data files plus execution time but combining compile times in there doesn't make much sense. You always have to pay for it in julia but not with a statically compiled file.

Re: Julia 1.6 Highlights

#59

Are the performance claims of Julia greatly exaggerated? Julia loses almost consistently to Go, Crystal, Nim, Rust, Kotlin, Python (PyPy, Numpy): https://github.com/kostya/benchmarks Is this because of bad typing or they didn't use Julia properly in idiomatic manner?

This appears to be a set of benchmarks of how fast a brainfuck interpreter implemented in different programming languages is on a small set of brainfuck programs? What a bizarre thing to care about benchmarks for. Are you planning on using Julia by writing brainfuck code and then running it through an interpreter written in Julia?

Re: Julia 1.6 Highlights

#60

Are the performance claims of Julia greatly exaggerated? Julia loses almost consistently to Go, Crystal, Nim, Rust, Kotlin, Python (PyPy, Numpy): https://github.com/kostya/benchmarks Is this because of bad typing or they didn't use Julia properly in idiomatic manner?

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