cffi VS julia

When you want to do anything that breaks the nice bubble of your Lisp image, you might want to know a bit about your operating system's programming interface. This will come in handy if you ever need to wrap a library with CFFI. There are some things that are pretty inconvenient as a rule (like dealing with any protocol that uses network byte order), but if you stay within the bubble of your Lisp image, you won't really notice them.

I'm just posting the work that I have done over the last year or so on CL-Objc. I'm still blocked from better support (e.g., passing structs by value for frameworks like UIKit). I just wanted to post what I have done online for others interested in the work or motivated to collaborate on this.

Good morning ladies and gentlemen, I have been waiting on some feedback for PR in CFFI. This feature is blocking me from reviving CL-OBJC. Any help will be appreciated. Thank you in advance.

Is that actually https://github.com/cffi/cffi/commit/33970351e71bb5f12ba56fc40270089e948ae112 ? I.e. after loading cl+ssl. (Although Hunchentoot does not interrupt threads)

Maybe access the pins using CFFI, https://github.com/cffi/cffi package and one the libraries mentioned here? https://www.bigmessowires.com/2018/05/26/raspberry-pi-gpio-programming-in-c/

FTR, on my system QL fetches CFFI 0.23.0 and the fix/error I'm talking about is https://github.com/cffi/cffi/blob/master/src/libraries.lisp#L106 and seems to have been added iin this PR https://github.com/cffi/cffi/pull/173/commits/263b38f4f2600dbacde8f2b313620c35a563c6df so the fix should be in CFFI 0.24.0 released 24 March 2021.

FTR: this is the PR https://github.com/cffi/cffi/pull/173/commits/263b38f4f2600dbacde8f2b313620c35a563c6df

C: Alternatively (more difficult) you could try to wrap the underlying C layers of either of those mentioned under Python with CFFI. The C-based game engine, Raylib, is also wrappable this way. I finished a super cool walking simulator in CL with that, but it is more tedious than the others since raylib is really barebones.

I feel inspired to start Lisp after being disappointed with the "open" source scene of 2021. I'd rather pay LispWorks a yearly fee and be left alone than dealing with unbalanced people in the Python space. The free Lisp implementations also look somewhat isolated from the ideological wars.

However, a C interface is required. Is this one the recommended solution? Is it really portable?

https://common-lisp.net/project/cffi/

What is the speed compared to a Python C extension? Are implementation-specific C interfaces faster (I guess they are)?

Sorry for so many questions, but these can usually only be answered by people who have actually used the interface.

34. Julia - $74,963

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.

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!

https://github.com/JuliaLang/julia/blob/release-1.10/NEWS.md

Visit official site: https://julialang.org/

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

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".

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

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.

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...