numericals
polymorphic-functions
numericals | polymorphic-functions | |
---|---|---|
6 | 6 | |
47 | 50 | |
- | - | |
7.7 | 5.6 | |
about 1 month ago | 29 days ago | |
Common Lisp | Common Lisp | |
MIT License | - |
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numericals
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numericals - Performance of NumPy with the goodness of Common Lisp
How about the semantics? Nevermind, I looked -- utter nonsense, just like numpy.
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Good Lisp libraries for math
Then there is a question - do you actually need these libraries? You can optimize code in Common Lisp (type declarations, usage of appropriate data structures, SIMD instructions etc). See this: https://github.com/digikar99/numericals/tree/master/sbcl-numericals <- SIMD instructions used from SBCL (on x86; these are processor-family specific so Apple M1 will have different ones).
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Image classification in CL? Help with starting point
*I have not; I have a couple of WIP/alpha-stage libraries like dense-arrays and numericals that could be useful; once I find the time, I want to think about if these or its dependencies can be integrated into the existing libraries including antik mentioned by awesome-cl.
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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.
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polymorphic-functions - Possibly AOT dispatch on argument types with support for optional and keyword argument dispatch
I made this while running into code modularity issues with the numericals project I attempted last year; I did discover specialization-store, but found its goals in conflict with what I wanted to achieve; so I ended up investing in this.
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:
What are some alternatives?
cl-cuda - Cl-cuda is a library to use NVIDIA CUDA in Common Lisp programs.
lisp-interface-library - LIL: abstract interfaces and supporting concrete data-structures in Common Lisp
py4cl - Call python from Common Lisp
fast-generic-functions - Seal your generic functions for an extra boost in performance.
py4cl2 - Call python from Common Lisp
cl-parametric-types - (BETA) C++-style templates for Common Lisp
specialization-store - A different type of generic function for common lisp.
ctype - CL type system implementation
Petalisp - Elegant High Performance Computing
cffi - The Common Foreign Function Interface
dense-arrays - Numpy like array object for common lisp
generic-cl - Generic function interface to standard Common Lisp functions