seL4 | julia | |
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60 | 350 | |
4,549 | 44,534 | |
1.2% | 0.5% | |
9.0 | 10.0 | |
3 days ago | 5 days ago | |
C | 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.
seL4
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From L3 to seL4 what have we learnt in 20 years of L4 microkernels? [video]
> People like to snob Unix but the fact is: the world runs on Unix.
The world you are aware of runs on it.
> Can we really do that much better or is it just hubris?
Yes. Have a look at seL4[1] and Barrelfish too[2], even though that's no longer active. seL4 in particular is powering a lot of highly secure computing systems. There is a surprisingly large sphere outside of Unix/POSIX.
[1] https://sel4.systems/
[2] https://barrelfish.org/
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On the Costs of Syscalls
There are also RTOS-capable microkernels such as seL4[0], with few but extremely fast syscalls[1]. Note times are in cycles, not usec.
0. https://sel4.systems/
1. https://sel4.systems/About/Performance/
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Can the language of proof assistants be used for general purpose programming?
https://sel4.systems
Working on a number of platforms, verified on some. Multicore support is an ongoing effort afaict.
On OS built on this kernel is still subject to some assumptions (like, hardware working correctly, bootloader doing its job, etc). But mostly those assumptions are less of a problem / easier to prove than the properties of a complex software system.
As I understand it, guarantees that seL4 does provide, go well beyond anything else currently out there.
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How to write TEE/Trusted OS for ARM microcontrollers?
Take a look at this: https://sel4.systems/
- Simulation: KI-Drohne der US Air Force eliminiert Operator für Punktemaximierung
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Paragon Graphite is a Pegasus spyware clone used in the US
It's probably have to be seL4 (https://sel4.systems), running on some fully OSS hardware.
There are question marks over much of available RISC-V chips due to chinese producers, so maybe OpenPower based hardware?
Plus, the entire system (motherboard, etc) would need to be manufactured using a good supply chain.
Hmmm, this has probably all been thought through in depth before by others. :)
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Basic SAT model of x86 instructions using Z3, autogenerated from Intel docs
You can use it to (mostly) validate small snippets are the same. See Alive2 for the application of Z3/formalization of programs as SMT for that [1]. As far as I'm aware there are some problems scaling up to arbitrarily sized programs due to a lack of formalization in higher level languages in addition to computational constraints. With a lot of time and effort it can be done though [2].
1. https://github.com/AliveToolkit/alive2
2. https://sel4.systems/
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What are the current hot topics in type theory and static analysis?
Formal methods. This is not in most general-purpose programming languages and probably never will be (maybe we'll see formal methods to verify unsafe code in Rust...) because it's a ton of boilerplate (you have to help the compiler type-check your code) and also extremely complicated. However, formal methods is very important for proving code secure, such as sel4 (microkernel formally verified to not have bugs or be exploitable) which has just received the ACM Software Systems Award 3 days ago.
- Rust Now Available for Real-Time Operating System and Hypervisor PikeOS
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Amiga and AmigaOS should move to ARM.
Today we'd look at seL4.
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?
l4v - seL4 specification and proofs
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
fprime - F´ - A flight software and embedded systems framework
NetworkX - Network Analysis in Python
nomicon - The Dark Arts of Advanced and Unsafe Rust Programming
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.
CompCert - The CompCert formally-verified C compiler
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
InitWare - The InitWare Suite of Middleware allows you to manage services and system resources as logical entities called units. Its main component is a service management ("init") system.
Numba - NumPy aware dynamic Python compiler using LLVM
4.4BSD-Lite2 - 4.4BSD Lite Release 2: last Unix operating system from Berkeley
F# - Please file issues or pull requests here: https://github.com/dotnet/fsharp