higgsml
magicl
| higgsml | magicl | |
|---|---|---|
| 2 | 14 | |
| 125 | 226 | |
| - | 0.4% | |
| 10.0 | 5.4 | |
| about 9 years ago | 6 months ago | |
| Common Lisp | Common Lisp | |
| MIT License | BSD 3-clause "New" or "Revised" License |
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higgsml
magicl
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A tutorial quantum interpreter in 150 lines of Lisp
(Link didn't work for me)
https://github.com/quil-lang/magicl/blob/master/src/high-lev...
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Why Lisp?
use MAGICL. [1] It is optionally and transparently accelerated by BLAS/LAPACK.
[1] https://github.com/quil-lang/magicl/blob/master/doc/high-lev...
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How fast can you multiply matrices using only common lisp?
Maybe have a look at how magicl does this?
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A software engineer's circuitous journey to calculate eigenvalues
This is essentially the first option, which is already supported by MAGICL by loading MAGICL/EXT-LAPACK [1].
[1] https://github.com/quil-lang/magicl#extensions
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Uncle Stats Wants You
I think what the magicl team has done is brilliant - allowing multiple implementations is awesome.
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Good Lisp libraries for math
Second up is magicl, especially useful if performance is a concern. This might not be as extensive as numcl, but it's been battle tested in the industry over the last decade or so. Because this uses generic functions, so long as you are using not-very-small arrays, performance should not be a concern for you. And even if you are, you could write your own functions that use the low-level functions that magicl's backends define. Otherwise performance can be at par with numpy.
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Why is python numpy *so* much faster than lisp in this example?
This Dev How-To describes (I hope in enough detail) how to add these specialized routines to MAGICL.
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CL-AUTOWRAP generated (C)BLAS wrapper in QUICKLISP
I agree... and I do don't want be the person who has not rallied. I just took a look at guicho's issue from 2019. And here, you yourself have admitted that the high level interface is less than ideal and needs more work. However, the very point that magicl is an industry standard could imply that potentially radical backward-incompatible changes can be hard. But, honestly, I want to discuss this, time permitting!
- Fast and Elegant Clojure: Idiomatic Clojure without sacrificing performance
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Anybody using Common Lisp or clojure for data science
Common Lisp is a great language to build new tools for data science, but currently has pretty awful library support existing data science workflows. Common Lisp is sorely lacking in high-quality statistics, plotting, and sparse arrays. There’s been a long work-in-progress library to bring flexible and high-performance linear algebra to Lisp, but it needs more contributors.
What are some alternatives?
JWM - Cross-platform window management and OS integration library for Java
lisp-matrix - A matrix package for common lisp building on work by Mark Hoemmen, Evan Monroig, Tamas Papp and Rif.
hash-array-mapped-trie-poly - A hash array mapped trie implementation in c.
py4cl - Call python from Common Lisp
http4s-native-image - Compiling an example http4s web service to a native executable using GraalVM Native Image
criterium - Benchmarking library for clojure
mgl - Common Lisp machine learning library.
Petalisp - Elegant High Performance Computing
hash-array-mapped-trie - A hash array mapped trie implementation in c.
april - The APL programming language (a subset thereof) compiling to Common Lisp.