LASS
Petalisp
LASS | Petalisp | |
---|---|---|
2 | 17 | |
102 | 445 | |
- | - | |
4.6 | 8.5 | |
3 months ago | 8 days ago | |
Common Lisp | Common Lisp | |
zlib License | GNU Affero General Public License v3.0 |
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LASS
- Spinneret: A modern Common Lisp HTML generator
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Common Lisp Implementations in 2023
There was a great comment about LispWorks over on the reddit discussion, linked here[0]. I really need to give it a shot at some point, especially as someone doing CL professionally.
I know that Lisp is popular on HN but that it's mostly a kind of zoo like experience where the proper devs come here to gawk at us but I really cannot recommend it enough for any kind of work. We use it for stock market analysis but almost every piece of code we write is CL. I'm currently trying to convince people to switch over our CSS over to LASS[1].
0: https://www.reddit.com/r/Common_Lisp/comments/11979q4/commen...
1: https://github.com/Shinmera/LASS
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.