JWM VS Petalisp

Compare JWM vs Petalisp and see what are their differences.

JWM

Cross-platform window management and OS integration library for Java (by HumbleUI)

Petalisp

Elegant High Performance Computing (by marcoheisig)
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JWM Petalisp
4 19
552 462
1.6% -
5.8 8.0
12 days ago about 2 months ago
C++ Common Lisp
Apache License 2.0 GNU Affero General Public License v3.0
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.

JWM

Posts with mentions or reviews of JWM. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-12-08.
  • Running IntelliJ IDEA with JDK 17 for Better Render Performance with Metal
    2 projects | /r/java | 8 Dec 2021
  • Fast and Elegant Clojure: Idiomatic Clojure without sacrificing performance
    14 projects | news.ycombinator.com | 23 Oct 2021
    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
    4 projects | news.ycombinator.com | 9 Sep 2021
  • The web is swallowing the desktop whole and nobody noticed (2017)
    3 projects | news.ycombinator.com | 9 Sep 2021

Petalisp

Posts with mentions or reviews of Petalisp. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-06-21.
  • Connection Machine Lisp (1986)
    3 projects | news.ycombinator.com | 21 Jun 2024
    Implementing lazy arrays or xappings naively is easy - the Petalisp reference backend has just 94 lines of code [1]. The challenge is to implement them efficiently. With eager evaluation, the programmer describes more or less precisely what the computer should do. With lazy evaluation, you only get a description of what should be done, with almost no no restriction on how to do it. To make lazy evaluation of arrays as fast as eager evaluation, you have to automate many of the high-level reasoning steps of an expert programmer. Doing so is extremely tedious, but once you have it you can write really clean and simple programs and still get the performance of hand-crafted wizard codes.

    [1] https://github.com/marcoheisig/Petalisp/blob/master/code/cor...

  • Petalisp: Elegant High Performance Computing
    1 project | /r/patient_hackernews | 10 Jul 2023
    1 project | /r/hackernews | 10 Jul 2023
    2 projects | news.ycombinator.com | 9 Jul 2023
  • Is there a tutorial for automatic differentiation with petalisp?
    2 projects | /r/lisp | 29 Mar 2023
  • Is there a language with lisp syntax but C semantics?
    7 projects | /r/lisp | 1 Mar 2023
    While not "as fast as C" (C is not the absolute pinnacle of performance), Common Lisp is incredibly fast compared to the majority of programming languages around today. There is even a huge amount of ongoing work being done to make it faster still. We are seeing many interesting projects that make better use of the hardware in your computer (e.g. https://github.com/marcoheisig/Petalisp).
  • Common Lisp Implementations in 2023
    10 projects | news.ycombinator.com | 23 Feb 2023
    i think lisp-stat library is actually being developed. however one numerical cl library that doesnt get enough mention and is being constantly developed is petalisp for HPC

    https://github.com/marcoheisig/Petalisp

  • numericals - Performance of NumPy with the goodness of Common Lisp
    8 projects | /r/lisp | 2 Aug 2022
    However, if you have a lisp library that puts those semantics to use, then you could get it to employ magicl/ext-blas and cl-bmas to speed it up. (petalisp looks relevant, but I lack the background to compare it with APL.)
  • New Lisp-Stat Release
    11 projects | news.ycombinator.com | 21 Jul 2022
    > his means cl pagckages can be "done".

    this is true if there is nothing functional that can be added to a package. however its very much not true for ml frameworks right now. new things are being added all the time in the field. however even in the package i linked you have the necessary ingredients for any deep learning model: cuda and back propagation. the other person mentioned convolution which i think is pretty trivial to implement but still, if you expect everything for you to be ready made then you should probably stick to tf and pytorch. if you want to explore the cutting edge and push the boundaries then i think common lisp is a good tool. as an aside it might also be interesting to note that a common lisp package (Petalisp) is being used for high performance computing by a german university

    https://github.com/marcoheisig/Petalisp

  • The Julia language has a number of correctness flaws
    19 projects | news.ycombinator.com | 16 May 2022

What are some alternatives?

When comparing JWM and Petalisp you can also consider the following projects:

tiled - Flexible level editor

awesome-cl - A curated list of awesome Common Lisp frameworks, libraries and other shiny stuff.

datascript - Immutable database and Datalog query engine for Clojure, ClojureScript and JS

cl-cuda - Cl-cuda is a library to use NVIDIA CUDA in Common Lisp programs.

skiko - Kotlin Multiplatform bindings to Skia

StatsBase.jl - Basic statistics for Julia

criterium - Benchmarking library for clojure

magicl - Matrix Algebra proGrams In Common Lisp.

skija - Java bindings for Skia

lish - Lisp Shell

gio - Mirror of the Gio main repository (https://git.sr.ht/~eliasnaur/gio)

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