| Xecto | Petalisp | |
|---|---|---|
| 1 | 17 | |
| 42 | 424 | |
| - | - | |
| 0.0 | 8.5 | |
| about 2 years ago | about 2 months ago | |
| Common Lisp | Common Lisp | |
| BSD 3-clause "New" or "Revised" License | GNU Affero General Public License v3.0 |
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Xecto
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cbaggers/rtg-math - a selection of the math routines most commonly needed for making realtime graphics in lisp (2, 3 and 4 component vectors, 3x3 and 4x4 matrices, quaternions, spherical and polar coordinates). [2019]
Xecto - A library for regular array parallelism. [3-clause BSD][15].
Petalisp
- Petalisp: Elegant High Performance Computing
- Is there a tutorial for automatic differentiation with petalisp?
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Is there a language with lisp syntax but C semantics?
While not "as fast as C" (C is not the absolute pinnacle of performance), Common Lisp is incredibly fast compared to the majority of programming languages around today. There is even a huge amount of ongoing work being done to make it faster still. We are seeing many interesting projects that make better use of the hardware in your computer (e.g. https://github.com/marcoheisig/Petalisp).
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Common Lisp Implementations in 2023
i think lisp-stat library is actually being developed. however one numerical cl library that doesnt get enough mention and is being constantly developed is petalisp for HPC
https://github.com/marcoheisig/Petalisp
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numericals - Performance of NumPy with the goodness of Common Lisp
However, if you have a lisp library that puts those semantics to use, then you could get it to employ magicl/ext-blas and cl-bmas to speed it up. (petalisp looks relevant, but I lack the background to compare it with APL.)
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New Lisp-Stat Release
> his means cl pagckages can be "done".
this is true if there is nothing functional that can be added to a package. however its very much not true for ml frameworks right now. new things are being added all the time in the field. however even in the package i linked you have the necessary ingredients for any deep learning model: cuda and back propagation. the other person mentioned convolution which i think is pretty trivial to implement but still, if you expect everything for you to be ready made then you should probably stick to tf and pytorch. if you want to explore the cutting edge and push the boundaries then i think common lisp is a good tool. as an aside it might also be interesting to note that a common lisp package (Petalisp) is being used for high performance computing by a german university
https://github.com/marcoheisig/Petalisp
- The Julia language has a number of correctness flaws
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When a young programmer who has been using C for several years is convinced that C is the best possible programming language and that people who don't prefer it just haven't use it enough, what is the best argument for Lisp vs C, given that they're already convinced in favor of C?
One trick is that Common Lisp can generate and compile code at runtime, whereas static languages typically do not have a compiler available at runtime. This lets you make your own lazy person's JIT/staged compiler, which is useful if some part of the problem is not known at compile-time. Such an approach has been used at least for array munging, type munging and regular expression munging.
What are some alternatives?
physical-quantities - A common lisp library that provides a numeric type with optional unit and/or uncertainty for computations with automatic error propagation.
awesome-cl - A curated list of awesome Common Lisp frameworks, libraries and other shiny stuff.
rtg-math - common lisp library providing common math functions used in games
JWM - Cross-platform window management and OS integration library for Java
polisher - Infix notation to S-expression (Polish notation) translator for Common Lisp
cl-cuda - Cl-cuda is a library to use NVIDIA CUDA in Common Lisp programs.
april - The APL programming language (a subset thereof) compiling to Common Lisp.
magicl - Matrix Algebra proGrams In Common Lisp.
cepl - Code Evaluate Play Loop
lish - Lisp Shell
cl-ana - Free (GPL) Common Lisp data analysis library with emphasis on modularity and conceptual clarity.
StatsBase.jl - Basic statistics for Julia