py4cl
cl-cuda
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py4cl | cl-cuda | |
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21 | 5 | |
221 | 270 | |
- | - | |
2.3 | 0.0 | |
6 months ago | almost 3 years ago | |
Common Lisp | Common Lisp | |
GNU General Public License v3.0 or later | MIT License |
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py4cl
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Need recommendation for IPC with Go
py4cl and cl4py rely on uiop:launch-program and python's subprocess respectively. These are portable to the extent uiop and subprocess are portable and do not require any additional installation.
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Lisp-Stick on a Python
If you want to use Python libs from CL, see py4cl: https://github.com/bendudson/py4cl the other way around, calling your efficient CL library from Python: https://github.com/marcoheisig/cl4py/ There might be more CL libraries than you think! https://github.com/CodyReichert/awesome-cl (or at least a project sufficiently advanced on your field to join forces ;) )
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The German School of Lisp (2011)
FYI you can call Python from CL: https://github.com/bendudson/py4cl and CL from Python: https://github.com/marcoheisig/cl4py/
If you don't know Emacs, see other editors: https://lispcookbook.github.io/cl-cookbook/editor-support.ht... If you want the more Smalltalk-like experience I'd go with the free LispWorks version: it has many GUI panes that allow to watch and discover the state of the program.
I personally couldn't stay long with Hylang. You won't get CL niceties: more language features, performance, standalone binaries, interactive debugger (all the niceties of an image-based development)…
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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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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.
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Why Hy?
I encourage people to try out Common Lisp because, unlike with Hy, you will get: speed, ability to build binaries, truly interactive image-based development (yes, more interactive than ipython), more static type checks, more language features (no closures in Hy last time I checked), language stability… To reach to Python libs, you have https://github.com/bendudson/py4cl My comparison of Python and CL: https://lisp-journey.gitlab.io/pythonvslisp/
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Tutorial Series to learn Common Lisp quickly
> Not sure if such a thing already exists for CL
couple of solutions exist for this
https://github.com/bendudson/py4cl
https://github.com/pinterface/burgled-batteries
- Calling Python from Common Lisp
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(define (uwu) (display "nya~\n"))
Ahh, makes sense. Well, if you ever wanna steal some of python's thunder, libpython-clj worked great for me lol. Supposedly py4cl fills a similar role in Common Lisp.
cl-cuda
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Why Lisp? (2015)
> You can write a lot of macrology to get around it, but there's a point where you want actual compiler writers to be doing this
this is not the job of compiler writers (although writing macros is akin to writing a compiler but i do not think that this is what you mean). in julia the numerical programming packages are not part of the standard library and a lot of it is wrappers around C++ code especially when the drivers to the underlining hardware are closed-source [0]. also here is the similar library in common lisp [1]
[0] https://github.com/JuliaGPU/CUDA.jl
[1] https://github.com/takagi/cl-cuda
- Fast and Elegant Clojure: Idiomatic Clojure without sacrificing performance
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Hacker News top posts: Aug 14, 2021
A Common Lisp Library to Use Nvidia CUDA\ (0 comments)
- A Common Lisp Library to Use Nvidia CUDA
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Machine Learning in Lisp
Personally, I've been relying on the stream-based method using py4cl/2, mostly because I did not - and perhaps do not - have the knowledge and time to dig into the CFFI based method. The limitation is that this would get you less than 10000 python interactions per second. That is sufficient if you will be running a long running python task - and I have successfully run trivial ML programs using it, but any intensive array processing gets in the way. For this later task, there are a few emerging libraries like numcl and array-operations without SIMD (yet), and numericals using SIMD. For reasons mentioned on the readme, I recently cooked up dense-arrays. This has interchangeable backends and can also use cl-cuda. But barring that, the developer overhead of actually setting up native-CFFI ecosystem is still too high, and I'm back to py4cl/2 for tasks beyond array processing.
What are some alternatives?
py4cl2 - Call python from Common Lisp
numcl - Numpy clone in Common Lisp
magicl - Matrix Algebra proGrams In Common Lisp.
criterium - Benchmarking library for clojure
hy - A dialect of Lisp that's embedded in Python
numericals - CFFI enabled SIMD powered simple-math numerical operations on arrays for Common Lisp [still experimental]
libpython-clj - Python bindings for Clojure
hash-array-mapped-trie - A hash array mapped trie implementation in c.
coalton - Coalton is an efficient, statically typed functional programming language that supercharges Common Lisp.
rewrite - Automated mass refactoring of source code.
racket - The Racket repository
LoopVectorization.jl - Macro(s) for vectorizing loops.