numericals
CFFI enabled SIMD powered simple-math numerical operations on arrays for Common Lisp [still experimental] (by digikar99)
extensible-compound-types
User defined compound types in Common Lisp (by digikar99)
numericals | extensible-compound-types | |
---|---|---|
6 | 2 | |
47 | 10 | |
- | - | |
7.7 | 6.5 | |
about 1 month ago | about 1 month ago | |
Common Lisp | Common Lisp | |
MIT License | - |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
numericals
Posts with mentions or reviews of numericals.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-08-02.
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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.
extensible-compound-types
Posts with mentions or reviews of extensible-compound-types.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-08-02.
-
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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Compile-time array bound checks
(Shameless plug) I have recently been working on extensible-compound-types that might be useful. My main purpose with it was to bring "nice" custom array (and container) types without using satisfies-based hackery. But perhaps it can be put to use here.
What are some alternatives?
When comparing numericals and extensible-compound-types you can also consider the following projects:
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
hissp - It's Python with a Lissp.
dense-numericals - Numerical Computing library with https://github.com/digikar99/dense-arrays as the front-end