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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.
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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…
Is Julia really that much more performant than numpy?
Take a look at the link below, comparing a simple sum in different languages (among them Python and Numpy). The power of Julia is that there is no privileged code. Your code will be as fast as the base library.
http://nbviewer.jupyter.org/github/alanedelman/18.337_2017/b...
"...the majority of programmers are not developers." Could someone please explain the difference between the two terms? I've always used them synonymously.
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…
- libraries
- community
- tools
Are much more important.
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…
Stuff like the ODE library that Chris has, or JuMP (for mathematical programming) that Miles Lubin, Iain Dunning and Joey Huechette wrote, or a number of other packages are simply not available elsewhere. The Celeste project, for example, achieved 1.6 PetaFlop/sec of compute rate on half a million cores.
Programming languages are abstractions. Some abstractions are better for certain kind of problems. Many of the amazing libraries that are available in Julia would simply not be easy to do otherwise.
Think about it - if these were incremental advances and no serious community would ever form - then Chris would have never written his packages. Julia has seen over a million downloads. In my biased opinion, it is more than an incremental advance. I think the right approach is to understand where those advances are and apply it to the right kind of problems.
There is a balance to be struck between the two extremes of nothing new will ever get adopted, and everything should adopt anything new and shiny that comes out.
"...the majority of programmers are not developers." Could someone please explain the difference between the two terms? I've always used them synonymously.
Whenever I see Julia mentioned, I like to link to this blog post by Graydon Hoare (creator of Rust). https://graydon2.dreamwidth.org/189377.html
What would you say is the takeaway point for this discussion?
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Julia, like Dylan and Lisp before it, is a Goldilocks language. Done by a bunch of Lisp hackers who seriously know what they're doing.
It is trying to span the entire spectrum of its target users' needs, from numerical inner loops to glue-language scripting to dynamic code generation and reflection. And it's doing a very credible job at it. Its designers have produced a language that seems to be a strict improvement on Dylan, which was itself excellent. Julia's multimethods are type-parametric. It ships with really good multi-language FFIs, green coroutines and integrated package management. Its codegen is LLVM-MCJIT, which is as good as it gets these days.
To my eyes, Julia is one of the brightest spots in the recent language-design landscape; it's working in a space that really needs good languages right now; and it's reviving a language-lineage that could really do with a renaissance. I'm excited for its future.
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Julia supports indexing with arbitrary bases, and it generally works ecosystem-wide.
Oh, it does? I've taken to saying that "zero-based indexing – that's how Julia broke my heart", because at the time, I didn't get the impression that there was anything else in sight. How do I index based on zero?
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One-based indexing is not a semantic issue, it's a language-design decision you may disagree with.
Julia 0.5 introduced support for any indexing scheme you care to invent. 1-based 0-based 20-based https://docs.julialang.org/en/latest/devdocs/offset-arrays/
-Stan Kelly-Bootle
Is there a good use case for Julia outside the "math" community, when your alternatives wouldn't be R or Numpy, but Ruby or C#?
For the most part, no. The language and community are significantly math oriented. However, if you're someone who likes to learn languages just to broaden their horizons a little bit, Julia might be a good choice. It's one of the few languages in common use which has multimethods (the other major one being Common Lisp). Actually, in general Julia is surprisingly Lispy for a language with Algol-family syntax.
It's less surprising if you know there's still a version of the Julia REPL that works with sexpressions: https://youtu.be/dK3zRXhrFZY?t=5m58s