py4cl2
julia
py4cl2 | julia | |
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11 | 351 | |
40 | 44,569 | |
- | 0.6% | |
5.6 | 10.0 | |
16 days ago | 5 days ago | |
Common Lisp | Julia | |
GNU General Public License v3.0 or later | MIT License |
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py4cl2
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An Idea for Piggybacking Python (language) ecosystem
I... recently got that working: https://github.com/digikar99/py4cl2/tree/master/cffi - Yes, CFFI! Yes, passing CL array data by reference!
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Plotting
I ended up using a fair bit of matplotlib through college and with colleagues. I too don't want to use python, but I also don't like throwing away its libraries, and I'm too lazy to invest in other* plotting ecosystems. In effect, I use up using matplotlib through py4cl/2.
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numericals - Performance of NumPy with the goodness of Common Lisp
Note that it is not my aim to replace the python ecosystem; I think that is far too lofy a goal to be of any good. My original intention was to interoperate with python through py4cl/2 or the likes, but felt that one needs a Common Lisp library for "small" operations, while "large" operations can be offloaded to python libraries through py4cl/2.
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interested in learning lisp, (specifically for games, but also for everything else including tui and gui applications for linux. currently have next to no programming knowledge, can i get forwarded some resources and some tips on what exactly i should do? any videos i should watch?
Python: Blender and Panda3D (game engine used for Disney's Toontown way back when) are both scriptable with Python. I've been able to successfully call Panda from Py4CL2 (thanks digikar for the help with that), but I have not tried with Blender yet. I think it's doable.
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Rewrite Your Scripts In LISP - with Roswell
While you are at it I may as well mention https://github.com/digikar99/py4cl2
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Good Lisp libraries for math
If performance is absolutely not a concern, then third option is using python libraries through py4cl/2. To put it differently, if calling python from lisp is not the bottleneck, then this is a feasible option.
- Using Lisp as a Dynamic Library
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What are the advantages of Hy/Hissp over python bindings for CL/Clojure?
py4cl2 (not py4cl!) author here. From the v2.9.0 docs:
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Design patterns for Lisp interop with other languages?
py4cl and py4cl2 represent a fairly pragmatic example of method 1, using an OS child process to communicate back and forth with your python code. Python is fairly popular and well-enabled with libraries, so you can delegate things to python that leverage those libraries.
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Image classification in CL? Help with starting point
If you can structure your code so that data de/serialization is not a bottleneck, then you could access the python libraries using py4cl/2.
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?
py4cl - Call python from Common Lisp
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
cl-cuda - Cl-cuda is a library to use NVIDIA CUDA in Common Lisp programs.
NetworkX - Network Analysis in Python
numcl - Numpy clone in Common Lisp
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.
farolero - Thread-safe Common Lisp style conditions and restarts for Clojure(Script) and Babashka.
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
vega-lite - A concise grammar of interactive graphics, built on Vega.
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