polymorphic-functions
Petalisp
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polymorphic-functions | Petalisp | |
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polymorphic-functions
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Adding new types and operators to Lisp
If performance is a concern, then you would want to stick to CLHS provided simple-array and create appropriate types using deftype, and then dispatch on the types either by yourself, or by using something like polymorphic-functions and polymorph.maths.
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defgeneric and &rest
If you want to dispatch on vectors, you can try out polymorphic-functions which was made for the express purpose of dispatching on specialized arrays aka types rather than classes.
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numericals - Performance of NumPy with the goodness of Common Lisp
Since the past year or two, I have been working on numericals that aims to provide the speed of NumPy with the goodness of Common Lisp. In particular, this includes the use of dynamic variables, restarts, and compiler-notes wherever appropriate. It uses CLTL2 API (and may be slightly more) under the hood to provide AOT dispatch, but nothing stops you from combining it with JAOT dispatch provided by numcl/specialized-function. This also spawned a number of projects most notably polymorphic-functions to dispatch on types instead of classes and extensible-compound-types that allows one to define user defined compound types (beyond just the type-aliases enabled by deftype. Fortunately enough, interoperation between magicl, numcl and numericals/dense-numericals actually looks plausible!
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Common Lisp polymorphic stories.
Before reading this, please go and check out https://github.com/digikar99/polymorphic-functions which this project is fully based on. It's great.
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polymorphic-functions - Possibly AOT dispatch on argument types with support for optional and keyword argument dispatch
What I am calling parametric polymorphism is this test:
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?
lisp-interface-library - LIL: abstract interfaces and supporting concrete data-structures in Common Lisp
awesome-cl - A curated list of awesome Common Lisp frameworks, libraries and other shiny stuff.
fast-generic-functions - Seal your generic functions for an extra boost in performance.
JWM - Cross-platform window management and OS integration library for Java
cl-parametric-types - (BETA) C++-style templates for Common Lisp
cl-cuda - Cl-cuda is a library to use NVIDIA CUDA in Common Lisp programs.
ctype - CL type system implementation
magicl - Matrix Algebra proGrams In Common Lisp.
numericals - CFFI enabled SIMD powered simple-math numerical operations on arrays for Common Lisp [still experimental]
lish - Lisp Shell
cffi - The Common Foreign Function Interface
StatsBase.jl - Basic statistics for Julia