one-more-re-nightmare
julia
one-more-re-nightmare | julia | |
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12 | 350 | |
133 | 44,569 | |
0.0% | 0.5% | |
4.2 | 10.0 | |
10 months ago | about 11 hours ago | |
Common Lisp | Julia | |
BSD 2-clause "Simplified" License | MIT License |
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one-more-re-nightmare
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Needle: A DFA Based Regex Library That Compiles to JVM ByteCode
https://github.com/telekons/one-more-re-nightmare
And the pretty hard to find blog post about it:
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Regular Expressions make me feel like a powerful wizard- that's not a good thing
Depends on your regex engine, and your non-regex solution. My engine (shameless self-plug https://github.com/telekons/one-more-re-nightmare) rivals hand-written automata, having to load each character more-or-less* only once, and throws in vectorisation for simple search loops too. I would not want to write or maintain the generated code.
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Don't be lazy this month!
one-more-re-nightmare used to let you write Σ, but I then tried to search Greek stuff with it and it went wrong. So now there's...$ for all characters (since that's not used for end-of-line assertions).
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When a young programmer who has been using C for several years is convinced that C is the best possible programming language and that people who don't prefer it just haven't use it enough, what is the best argument for Lisp vs C, given that they're already convinced in favor of C?
One trick is that Common Lisp can generate and compile code at runtime, whereas static languages typically do not have a compiler available at runtime. This lets you make your own lazy person's JIT/staged compiler, which is useful if some part of the problem is not known at compile-time. Such an approach has been used at least for array munging, type munging and regular expression munging.
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Tutorial Series to learn Common Lisp quickly
> One of my favorite examples is the regex library cl-ppcre. Thanks to the nature of Lisp, the recognizer for each regex you create can be compiled to native code on compiler implementations of CL.
That is not true - cl-ppcre generates a chain of closures. Experimental performance is in the same ballpark as typical "bytecode" interpreting regex implementations.
(Disclosure: I wrote another regex library at <https://github.com/telekons/one-more-re-nightmare>, which does do native code compilation.)
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The self-hosted Zig compiler can now successfully compile itself
Someone else didn't tell me that before, so it can't be true. But I don't publish papers on toys, nor do I think toy projects are awfully fast. Though the x86-64 backend I wrote was in someone else's repository and thus was several PRs :(
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Most interesting languages to learn (from)?
Regular expressions
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Is regex really fast in CL?
Also try this https://github.com/telekons/one-more-re-nightmare
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Why You Should Learn Lisp In 2022?
A Common Lisp system has the compiler around at runtime, so if you can figure out how to profitably stage/specialise a computation, then you can roll your own cheap JIT of sorts. This can be useful for array munging and regular expressions at the least. You can do this in C, of course but you would need to use another compiler as a library (e.g. LLVM, TCC, libgccjit) or write your own (e.g. PCRE2's sljit).
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LISP with GC in 436 bytes
Agree to disagree - I don't have the energy to remember operator precedence. One file from the regular expression compiler has most of the rewrite rules I read from the papers, except in S-expression syntax. There were a few bugs due to misreading precedence. Also c.f. Gerald Sussman talking about physics notation being a pain in the butt.
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?
Revise.jl - Automatically update function definitions in a running Julia session
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
SICL - A fresh implementation of Common Lisp
NetworkX - Network Analysis in Python
zig - General-purpose programming language and toolchain for maintaining robust, optimal, and reusable software.
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.
cl-ppcre - Common Lisp regular expression library
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
oakc - A portable programming language with a compact intermediate representation
Numba - NumPy aware dynamic Python compiler using LLVM
Petalisp - Elegant High Performance Computing
F# - Please file issues or pull requests here: https://github.com/dotnet/fsharp