dense-arrays
Numpy like array object for common lisp (by digikar99)
numericals
CFFI enabled SIMD powered simple-math numerical operations on arrays for Common Lisp [still experimental] (by digikar99)
dense-arrays | numericals | |
---|---|---|
7 | 6 | |
23 | 47 | |
- | - | |
6.3 | 7.7 | |
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.
dense-arrays
Posts with mentions or reviews of dense-arrays.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-09-20.
- dense-arrays: Numpy like array object for common lisp
-
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.
-
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
Currently I have put successfully this to use at dense-numericals - which I created over dense-arrays after finding CL arrays to be not that suitable, as compared to numpy or julia. Now, dense-numericals relies on passing the array pointer to C functions. However, IIUC, this runs into issues for what if the GC moves the arrays while the computation is still not done; is this worry valid? I think I ran into this while running multithreaded tests on CCL, ending up in segfaults.
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Confused about array runtime type checking in SBCL
Shameless unstable plug: I think it should be possible to provide type checking with a different backend that does not upgrade the types at https://github.com/digikar99/dense-arrays - the backend things are themselves unstable though.
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Past, Present, and Future of Lisp
In semi-production, ideally the problems are best represented using state diagrams, but I don't see a way to comfortably represent graphs in textual formats. The best I see is list of lists, which doesn't feel significantly better than the spaghetti code it currently is (for instance this and this - but these are just about one function each in a larger system, so not totally worth a DSL, unless there existed a defacto state-diagram DSL which everyone could be expected to know.
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.
-
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).
-
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.
-
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.
-
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.
What are some alternatives?
When comparing dense-arrays and numericals you can also consider the following projects:
array-operations - Common Lisp library that facilitates working with Common Lisp arrays.
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
cl-parametric-types - (BETA) C++-style templates for Common Lisp
specialization-store - A different type of generic function for common lisp.
Petalisp - Elegant High Performance Computing
awesome-cl - A curated list of awesome Common Lisp frameworks, libraries and other shiny stuff.
specialized-function - Julia-like dispatch for Common Lisp
dense-arrays vs array-operations
numericals vs cl-cuda
dense-arrays vs py4cl
numericals vs py4cl
dense-arrays vs py4cl2
numericals vs py4cl2
dense-arrays vs cl-parametric-types
numericals vs specialization-store
dense-arrays vs specialization-store
numericals vs Petalisp
dense-arrays vs awesome-cl
numericals vs specialized-function