StaticArrays.jl
julia
StaticArrays.jl | julia | |
---|---|---|
6 | 350 | |
739 | 44,569 | |
1.2% | 0.6% | |
7.6 | 10.0 | |
about 1 month ago | 1 day ago | |
Julia | Julia | |
GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
StaticArrays.jl
- How to efficiently get the i-th element of every matrix in an array?
-
Error With StaticArrays Module & Symbolics.jl
Performance is already 100 times faster than Sympy, but I was running into slowdowns computing the adjugate of the matrices I'm working with. I turned to the StaticArrays.jl module, which did speed up performance, but threw an error at me when the matrix in question went from a 4x4 to a 5x5. Here is the code used to generate the matrices:
-
An optimization story
I know this is the Rust subreddit, but I have to ask if you considered Julia? It seems purpose-built for what you're trying to do. It has built-in multidimensional arrays which are significantly more ergonomic to use than either Python or Rust, and because of the way the type system works there are tons of specialized array types you can use with optimized operations (and writing your own is quite easy). In particular you might be interested in StaticArrays.jl which if your arrays have a known, small size at compile-time can give a massive speedup by essentially automatically doing many of the optimizations you did by hand. They show a 25x speedup on eigendecompositions for a 3x3 matrix on their microbenchmark.
-
Easy things to implement to optimize code?
Use StaticArrays.jl instead of the built-in Array whenever you need some statically sized array/matrix.
-
From Julia to Rust
2. Persistent data structures as found in most FP languages.
Note how neither of these capture the large, (semi-)contiguous array types used for most numerical computing. These arrays are only "easier to optimize" if one has a Sufficiently Smart Compiler to work with. Here we don't even need to talk about Julia: the reason even Numba kernels in Python land are written in a mutating style is because such a compiler does not exist. You may be able to define something for a limited subset of programs like TensorFlow does, but the moment you step outside that small closed world you're back to needing mutation and loops to get a reasonable level of performance. What's more, the fancy ML graph compiler (as well as Numpy and vectorized R) is dispatching to C++/Fortran/CUDA routines that, not surprisingly, are also loop-heavy and mutating.
Should Julia do a better job of trying to optimize non-mutating array operations? Most definitely. Is this a hard problem that has consumed untold FAANG developer hours [2] and spawned an entire LLVM subproject [3] to address it? Also yes.
> The iteration protocol can be made more memory-efficient for large collections and simpler...
Yup, this has been a consistent bugbear of the core team as well. The JuliaFolds ecosystem [4] offers a compelling alternative with fusion, automatic parallelism, etc. in line with that blog post (which, I should note, is a much different beast from Rust's iterator interface/Rayon), but it doesn't seem like the API will be changing until a breaking language release is planned.
> In general, systems languages don't make language decisions lightly. They have committees, discuss how other languages do things, make proposals. This allows more perspectives on each decision. That would be an improvement over the more ad-hoc style of Julia development, as long as Julia can avoid adding every possible feature, which is a risk of expanding the decision-making body.
I'd argue this is a property of mature, widely used languages instead of systems languages. Python, Ruby, JS, PHP, C# and Java are all examples of "non-systems" languages that do everything you list, while Nim and Zig (note: both less well adopted) are examples of "systems" languages that don't have such a formalized governance model.
Julia (along with Elixir) are somewhere in between: All design talk and decision making is public and relatively centralized on GitHub issues. There is no fixed RFC template, but proposals go through a lot of scrutiny from both the core team and community, as well as at least one round of a formal triage (run by the core team, but open to all). Any changes are also tested for backwards compat via PkgEval, which works much like Crater in Rust. There was a brief effort to get more structured RFCs [5], but I think it failed because the community just isn't large enough yet. Note how all the languages with a process like this are a) large, and b) developed it organically as the userbase grew. In other words, you'll probably see something similar pop up when the time savings provided by a more structured/formal process outweighs the overhead of additional formalization.
[1] https://github.com/JuliaArrays/StaticArrays.jl
-
Is there a reason type constraints can't be applied to value-types?
If you want to lift your value into the type domain, you may be able to use value-types, like Val(2). If you are interested in seeing how to use value-type parameters efficiently, https://github.com/JuliaArrays/StaticArrays.jl does that to great effect.
julia
-
Top Paying Programming Technologies 2024
34. Julia - $74,963
-
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.
-
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!
-
Julia 1.10 Highlights
https://github.com/JuliaLang/julia/blob/release-1.10/NEWS.md
-
Best Programming languages for Data Analysis📊
Visit official site: https://julialang.org/
-
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
-
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".
-
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
-
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.
-
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?
JET.jl - An experimental code analyzer for Julia. No need for additional type annotations.
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Catlab.jl - A framework for applied category theory in the Julia language
NetworkX - Network Analysis in Python
Symbolics.jl - Symbolic programming for the next generation of numerical 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.
SumTypes.jl - An implementation of Sum types in Julia
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
glow - Compiler for Neural Network hardware accelerators
Numba - NumPy aware dynamic Python compiler using LLVM
egg - egg is a flexible, high-performance e-graph library
F# - Please file issues or pull requests here: https://github.com/dotnet/fsharp