numcl
avm
| numcl | avm | |
|---|---|---|
| 9 | 2 | |
| 627 | 53 | |
| 0.3% | - | |
| 0.0 | 0.0 | |
| 7 months ago | over 2 years ago | |
| Common Lisp | Common Lisp | |
| GNU General Public License v3.0 or later | - |
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numcl
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How fast can you multiply matrices using only common lisp?
Is it me or numcl is faster than magicl? Matrix multiplication on magicl with pure lisp backend is
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Rewrite Your Scripts In LISP - with Roswell
Interesting, I will, thanks! I am aware of numcl for CL, but I don't think it is "there" yet :).
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Good Lisp libraries for math
The first that comes to mind is numcl. This works if (i) performance is not seriously a concern, (ii) you are not annoyed by julia-like JIT/JAOT compilation delays, (iii) copy-based slicing won't be a performance issue for you. To be fair, limitation (i) might be overcome by writing a better (simd-based) backend for numcl. numcl is fast, it compiles to fairly good code, but simd can boost the performance by another 4-8 times or so.
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Efficiently/easily sample from a list - any existing alternative?
am I missing something that already exists (numcl / Alexandria / core language, etc?)
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Lisp as an Alternative to Java (2000)
>Either implement numpy equivalent on your own or half of your code is data massaging data between libraries
I haven't tested this but here you go:
https://github.com/numcl/numcl
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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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cbaggers/rtg-math - a selection of the math routines most commonly needed for making realtime graphics in lisp (2, 3 and 4 component vectors, 3x3 and 4x4 matrices, quaternions, spherical and polar coordinates). [2019]
numcl - Numpy clone in Common Lisp. [LGPL3][9].
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SBCL: New in Version 2.1.0
[3] https://github.com/numcl/numcl
avm
- The Julia language has a number of correctness flaws
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cbaggers/rtg-math - a selection of the math routines most commonly needed for making realtime graphics in lisp (2, 3 and 4 component vectors, 3x3 and 4x4 matrices, quaternions, spherical and polar coordinates). [2019]
avm - Efficient and expressive arrayed vector math library with multi-threading and CUDA support. [MIT][200].
What are some alternatives?
cl-cuda - Cl-cuda is a library to use NVIDIA CUDA in Common Lisp programs.
Optimization.jl - Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.
april - The APL programming language (a subset thereof) compiling to Common Lisp.
polisher - Infix notation to S-expression (Polish notation) translator for Common Lisp
lisp-matrix - A matrix package for common lisp building on work by Mark Hoemmen, Evan Monroig, Tamas Papp and Rif.
StatsBase.jl - Basic statistics for Julia
Petalisp - Elegant High Performance Computing
cl-containers - Containers Library for Common Lisp
cepl - Code Evaluate Play Loop
magicl - Matrix Algebra proGrams In Common Lisp.
physical-quantities - A common lisp library that provides a numeric type with optional unit and/or uncertainty for computations with automatic error propagation.