magicl
JWM
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magicl | JWM | |
---|---|---|
14 | 4 | |
225 | 536 | |
0.4% | 2.2% | |
5.4 | 6.3 | |
6 months ago | 2 months ago | |
Common Lisp | C++ | |
BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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.
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.
JWM
- Running IntelliJ IDEA with JDK 17 for Better Render Performance with Metal
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Fast and Elegant Clojure: Idiomatic Clojure without sacrificing performance
sigh
Yeah. I am very bullish on Kotlin. Think it's probably the most exciting language evolving right now.
I went on a few-tweet minirant here about why:
https://twitter.com/GavinRayDev/status/1443279425311805440
But the tl;dr is that:
- There is Jetpack Compose currently, for Desktop, Web, and Android
- And Kotlin Native putting a large portion of resources into Skia bindings (JetBrains calls the lib "Skiko" for Kotlin Native https://github.com/JetBrains/skiko and "Skija")
It's very clear (and there are some employees which have confirmed this IIRC) that they are working on "Jetpack Compose Everywhere" that runs on iOS as well, from a single codebase.
There's the big Kotlin event going on right now, where they just announced the new WASM backend and changes in their compiler + IR commonizing/restructuring ("K2").
- https://blog.jetbrains.com/kotlin/2021/10/the-road-to-the-k2...
- https://www.youtube.com/watch?v=-pqz9sKXatw
The net result is that you wind up with a single language that you can use to write your backend API, your UI code (Jetpack Compose app deployed across Web/Android/iOS/Mac/Win/Linux, or transpile to JS/TS if you just want a web app, etc) and with Kotlin Native even your native, low-level code to integrate with existing C/C++ etc ecosystem.
KN already does automatic bindgen for C and Swift headers, they have direct C++ interop (like Swift does) on their future roadmap as a potential "todo".
All of this is mostly possible already -- I can do the same thing using IE Java, GraalVM, and a transpiler like Google's j2cl or bck2brwser (which is what Gluon uses for JavaFX on the web). Including the "native" part.
IE, here's a contribution I made to get GraalVM producing native binaries using Skia from the JVM + JNI Jetbrains Skia library:
https://github.com/HumbleUI/JWM/issues/158
But Kotlin is pushing the hardest to make this whole platform/stack from native <-> desktop <-> mobile <-> browser a seamless, unified experience. And you can feel it, when you try to do the "whole stack, every platform, one language" thing.
Sorry for the rant and wall of text!
- Thoughts on Clojure UI framework
- The web is swallowing the desktop whole and nobody noticed (2017)
What are some alternatives?
lisp-matrix - A matrix package for common lisp building on work by Mark Hoemmen, Evan Monroig, Tamas Papp and Rif.
tiled - Flexible level editor
py4cl - Call python from Common Lisp
datascript - Immutable database and Datalog query engine for Clojure, ClojureScript and JS
criterium - Benchmarking library for clojure
skiko - Kotlin MPP bindings to Skia
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.
skija - Java bindings for Skia