Microbenchmarks
julia
Microbenchmarks | julia | |
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1 | 351 | |
84 | 44,569 | |
- | 0.6% | |
2.3 | 10.0 | |
4 months ago | 5 days ago | |
Jupyter Notebook | Julia | |
GNU General Public License v3.0 or later | MIT License |
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Microbenchmarks
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Romeo and Julia, Where Romeo Is Basic Statistics
> Every language I've ever seen with garbage collection has gone through decades of "now the garbage collection is better" or "just wait until the next version, garbage collection will be better".
Ok but the Go example I linked is already in production, right now, you can use it. This isn't a "it will get better in two releases" situation, Go's GC as of today has pause times that are sub-millisecond. The Java Shenandoah example I linked is still mostly in beta, but it's also something you can use right now, though admittedly it'll probably be awhile before it's in a mainline release.
> This is besides the point of performance and no longer talking about reality, it's just FUD from a "what if" future.
It's not "just FUD", there are dozens of reported security issues that have happened because of bad manual memory management problems. Off the top of my head, Heartbleed was a famous case.
This isn't me badmouthing anyone; manual memory management is hard to get right, even for very smart people.
> Right, but you get it by avoiding allocation and avoiding the garbage collector the same way avoiding allocation in C++ is important, but in julia it won't be woven in to the performance, it will cause big pauses.
Fair enough, I did look at the code for the official benchmarks (https://github.com/JuliaLang/Microbenchmarks/blob/master/per...) and outside of the integer parsing code it does indeed seem to avoid dynamic allocations so I will concede that the benchmarks might be a bit more skewed compared to real-world code.
I still get a hunch that if you compared it allocation-heavy Julia to malloc+free-heavy C++ the differences wouldn't really be that far off, but that's just a hunch and I don't have data to back that up; might be a fun test to write though, so maybe I'll try that this weekend.
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Sort of tangential, but I also do think that there's value in having decent concurrency constructs built into the language. With C++, if you stick to built-ins you are basically stuck with mutexes and despite what people like to pretend, getting correct code with mutexes is really really hard to get right, and very easy to screw up in a non-obvious way. If you allow yourself to use libraries, then you have stuff like ZeroMQ and OpenMP and stuff, so it's really not that dire realistically. However, I think there's value in having nice, easy to use concurrency constructs in the language other than mutexes, and I do wonder if as a result of that it encourages people to utilize multiple threads more frequently, because they don't have to worry about weird deadlock situations as much.
Again, I believe Rust actually does address this because of the single-owner-enforced-at-compile-time stuff, but I haven't used it enough to really draw a conclusion on it.
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?
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
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).
LUA - A programming language based upon the lua programming language
PackageCompiler.jl - Compile your Julia Package
femtolisp - a lightweight, robust, scheme-like lisp implementation
JLD2.jl - HDF5-compatible file format in pure Julia