numericals
cl-blapack
numericals | cl-blapack | |
---|---|---|
6 | 1 | |
47 | 15 | |
- | - | |
7.7 | 10.0 | |
about 1 month ago | about 7 years ago | |
Common Lisp | Common Lisp | |
MIT License | BSD 3-clause "New" or "Revised" 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.
cl-blapack
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Good Lisp libraries for math
Most of the implementations of Common Lisp support some form of calling C libraries, why not to call these libraries directly if you need them? There is a plethora of Github repositories from people who tried to wrap BLAS/LAPACK only to realise how much effort it is to write all the bindings and maintain them (e.g. here: https://github.com/blindglobe/cl-blapack ). You can take inspiration from it, though.
What are some alternatives?
cl-cuda - Cl-cuda is a library to use NVIDIA CUDA in Common Lisp programs.
py4cl - Call python from Common Lisp
py4cl2 - Call python from Common Lisp
specialization-store - A different type of generic function for common lisp.
Petalisp - Elegant High Performance Computing
dense-arrays - Numpy like array object for common lisp
specialized-function - Julia-like dispatch for Common Lisp
polymorphic-functions - A function type to dispatch on types instead of classes with partial support for dispatching on optional and keyword argument types.
numcl - Numpy clone in Common Lisp
extensible-compound-types - User defined compound types in Common Lisp
hissp - It's Python with a Lissp.
dense-numericals - Numerical Computing library with https://github.com/digikar99/dense-arrays as the front-end