rhombus-prototype
julia
rhombus-prototype | julia | |
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24 | 350 | |
299 | 44,534 | |
0.7% | 0.5% | |
9.7 | 10.0 | |
7 days ago | 3 days ago | |
Racket | Julia | |
GNU General Public License v3.0 or later | MIT License |
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rhombus-prototype
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Why does Racket have Type-Maps instead of Just a Single Map?
See related post. The dot operator in Rhombus will allow a function call like expr.map(…) to be statically specialized to Some.map(expr, …) provided that expr carries sufficient static information. This isn’t possible in Racket given the lack of static information in general.
- State of Rhombus
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Rhombus-in-the-rough: A 2D RPG implemented in the Rhombus Racket dialect
If you want to know more the best starting point is https://github.com/racket/rhombus-prototype They have discussion on the GitHub repo
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Multiple namespaces?
Racket has the concept of binding space built on top of the scope-set model. The experiment language Rhombus makes heavy use of this for contextual bindings. Note that bindings are used for language extensions among other purposes in Racket.
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Generalized and first-class macros: what is this called?
The notion of “tail sequence” in general doesn’t exist in Lisp’s macro-expansion model, since Lisp macros are strictly local transformations. A “tail sequence” allows a macro to control the expansion of the whole context, which requires wrapping the whole context in another macro in Lisp’s model. This is what leads to proposals like #%local-definition in Racket. However, this notion does exist in the enforestation model, which is what the experiment language Rhombus is based on, although it’s probably not quite a Lisp ;)
- Lang Rhombus
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Anyone else concerned that Rhombus/Racket2 is not a lisp based language?
Rhombus is: - just another #lang. It is built on top of the existing Racket VM and written in Racket. It interoperates with existing Racket code and uses the Racket expander. - macro extendable. Hygiene and all of the good stuff work. - being developed in the open. We meet biweekly over Zoom, and discussions also occur in GitHub Discussions.
- Anyone aware of Racket projects that are in need of contributors? I am experienced in PL design and have two months worth of spare time. I have never contributed to an opensource project before besides taureg.
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Racket->Rhombus: To Sexp or not to Sexp?
Querying Git references for rhombus-prototype at https://github.com/racket/rhombus-prototype.git Using cached16617263581661726358301 for https://github.com/racket/rhombus-prototype.git DrRacket install: version mismatch for dependency for package: https://github.com/racket/rhombus-prototype.git mismatch packages: base (have 8.6, need 8.6.0.9)
Instead of hoping, you might consider reading the discussions to see what the developers are actually saying. Just a thought.
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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Julia 1.10 Highlights
https://github.com/JuliaLang/julia/blob/release-1.10/NEWS.md
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Best Programming languages for Data Analysis📊
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
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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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Eleven strategies for making reproducible research the norm
I have asked about Julia's reproducibility story on the Guix mailing list in the past, and at the time Simon Tournier didn't think it was promising. I seem to recall Julia itself didnt have a reproducible build. All I know now is that github issue is still not closed.
https://github.com/JuliaLang/julia/issues/34753
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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...
What are some alternatives?
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F# - Please file issues or pull requests here: https://github.com/dotnet/fsharp