julia
Pluto.jl
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julia | Pluto.jl | |
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350 | 78 | |
44,469 | 4,860 | |
0.7% | - | |
10.0 | 9.4 | |
about 7 hours ago | 6 days ago | |
Julia | JavaScript | |
MIT License | MIT License |
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julia
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Top Paying Programming Technologies 2024
34. Julia - $74,963
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Optimize sgemm on RISC-V platform
I don't believe there is any official documentation on this, but https://github.com/JuliaLang/julia/pull/49430 for example added prefetching to the marking phase of a GC which saw speedups on x86, but not on M1.
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Dart 3.3
3. dispatch on all the arguments
the first solution is clean, but people really like dispatch.
the second makes calling functions in the function call syntax weird, because the first argument is privileged semantically but not syntactically.
the third makes calling functions in the method call syntax weird because the first argument is privileged syntactically but not semantically.
the closest things to this i can think of off the top of my head in remotely popular programming languages are: nim, lisp dialects, and julia.
nim navigates the dispatch conundrum by providing different ways to define free functions for different dispatch-ness. the tutorial gives a good overview: https://nim-lang.org/docs/tut2.html
lisps of course lack UFCS.
see here for a discussion on the lack of UFCS in julia: https://github.com/JuliaLang/julia/issues/31779
so to sum up the answer to the original question: because it's only obvious how to make it nice and tidy like you're wanting if you sacrifice function dispatch, which is ubiquitous for good reason!
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Best Programming languages for Data Analysis📊
🌟 Visit Github
Visit official site: https://julialang.org/
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Potential of the Julia programming language for high energy physics computing
No. It runs natively on ARM.
julia> versioninfo() Julia Version 1.9.3 Commit bed2cd540a1 (2023-08-24 14:43 UTC) Build Info: Official https://julialang.org/ release
The one you need is this which is already merged but was after the 1.10 feature freeze so it has to wait till 1.11, though you can test it with nightly builds which is available on julialang site: https://github.com/JuliaLang/julia/pull/51435
Unfortunately, the core devs are not too chatty about standalone binaries, because of how Julia's internals are set there are going to be a lot of unforeseen challenges, so they are not trying to promise how things will be rather let's wait and see how things will turnout. Since packagecompiler.jl already has C ABI and one goal discussed about binaries being easily callable from other languages and vice versa, I would bet that it will have shared libraries.
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Rust std:fs slower than Python
https://github.com/JuliaLang/julia/issues/51086#issuecomment...
So while this "fixes" the issue, it'll introduce a confusing time delay between you freeing the memory and you observing that in `htop`.
But according to https://jemalloc.net/jemalloc.3.html you can set `opt.muzzy_decay_ms = 0` to remove the delay.
Still, the musl author has some reservations against making `jemalloc` the default:
https://www.openwall.com/lists/musl/2018/04/23/2
> It's got serious bloat problems, problems with undermining ASLR, and is optimized pretty much only for being as fast as possible without caring how much memory you use.
With the above-mentioned tunables, this should be mitigated to some extent, but the general "theme" (focusing on e.g. performance vs memory usage) will likely still mean "it's a tradeoff" or "it's no tradeoff, but only if you set tunables to what you need".
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Julia as a unifying end-to-end workflow language on the Frontier exascale system
I don't really know what kind of rebuttal you're looking for, but I will link my HN comments from when this was first posted for some thoughts: https://news.ycombinator.com/item?id=31396861#31398796. As I said, in the linked post, I'm quite skeptical of the business of trying to assess relative buginess of programming in different systems, because that has strong dependencies on what you consider core vs packages and what exactly you're trying to do.
However, bugs in general suck and we've been thinking a fair bit about what additional tooling the language could provide to help people avoid the classes of bugs that Yuri encountered in the post.
The biggest class of problems in the blog post, is that it's pretty clear that `@inbounds` (and I will extend this to `@assume_effects`, even though that wasn't around when Yuri wrote his post) is problematic, because it's too hard to write. My proposal for what to do instead is at https://github.com/JuliaLang/julia/pull/50641.
Another common theme is that while Julia is great at composition, it's not clear what's expected to work and what isn't, because the interfaces are informal and not checked. This is a hard design problem, because it's quite close to the reasons why Julia works well. My current thoughts on that are here: https://github.com/Keno/InterfaceSpecs.jl but there's other proposals also.
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Getaddrinfo() on glibc calls getenv(), oh boy
Doesn't musl have the same issue? https://github.com/JuliaLang/julia/issues/34726#issuecomment...
I also wonder about OSX's libc. Newer versions seem to have some sort of locking https://github.com/apple-open-source-mirror/Libc/blob/master...
but older versions (from 10.9) don't have any lockign: https://github.com/apple-oss-distributions/Libc/blob/Libc-99...
Pluto.jl
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Potential of the Julia programming language for high energy physics computing
I thought that notebook based development and package based development were diametrically opposed in the past, but Pluto.jl notebooks have changed my mind about this.
A Pluto.jl notebook is a human readable Julia source file. The Pluto.jl package is itself developed via Pluto.jl notebooks.
https://github.com/fonsp/Pluto.jl
Also, the VSCode Julia plugin tooling has really expanded in functionality and usability for me in the past year. The integrated debugging took some work to setup, but is fast enough to drop into a local frame.
https://code.visualstudio.com/docs/languages/julia
Julia is the first language I have achieved full life cycle integration between exploratory code to sharable package. It even runs quite well on my Android. 2023 is the first year I was able to solve a differential equation or render a 3D surface from a calculated mesh with the hardware in my pocket.
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Ask HN: Why don't other languages have Jupyter style notebooks?
Re Julia there is also pluto.jl that is another notebook-like environment for julia. It's been a few years since I played with it but it looked cool, for example it handles state differently so you don't get into the same messes as with ipython notebooks. https://plutojl.org/
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Looking for a Julia gui framework with a demo like EGUI
For this, Notebooks are often used. Julia offers a uniquely nice and interactive Pluto notebook for the web https://github.com/fonsp/Pluto.jl
- Excel Labs, a Microsoft Garage Project
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IPyflow: Reactive Python Notebooks in Jupyter(Lab)
I believe this is what Pluto sets out to do for Julia.
I used it as part of the “Computational Thinking” with Julia course a year or two back. Even then the beta software was very good and some of the demos the Pluto dev showed were nothing short of amazing
- For Julia is there some thing like VSCode's python interactive window?
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What have you "washed your hands of" in Python?
I think what you want is Pluto!
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Show HN: Out of order execution in Jupyter notebooks is a solved problem
I like how Pluto.jl handles this:
> Pluto offers an environment where changed code takes effect instantly and where deleted code leaves no trace. Unlike Jupyter or Matlab, there is no mutable workspace, but rather, an important guarantee:
> At any instant, the program state is completely described by the code you see.
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My Journey from R to Julia
I only used Julia for a short time, but I didn't see the blazing fast speeds I was promised. I've seen the benchmarks, of course, on which the claims are founded, but the C-like speeds weren't obvious to me in everyday data science workflows. In the end, there wasn't sufficient motivation for me to switch to Julia as my weapon of choice. I do like Pluto[0], though...
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Using Julia with Anaconda and Voilà
You can use https://github.com/fonsp/Pluto.jl instead of Jupyter.
What are some alternatives?
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
NetworkX - Network Analysis in Python
Lua - Lua is a powerful, efficient, lightweight, embeddable scripting language. It supports procedural programming, object-oriented programming, functional programming, data-driven programming, and data description.
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
Numba - NumPy aware dynamic Python compiler using LLVM
vim-slime - A vim plugin to give you some slime. (Emacs)
F# - Please file issues or pull requests here: https://github.com/dotnet/fsharp
StaticCompiler.jl - Compiles Julia code to a standalone library (experimental)
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).
rmarkdown - Dynamic Documents for R
Weave.jl - Scientific reports/literate programming for Julia
Dash.jl - Dash for Julia - A Julia interface to the Dash ecosystem for creating analytic web applications in Julia. No JavaScript required.