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marcoheisig
Petalisp
Common Lisp2018–2026
marcoheisig / Petalisp

Petalisp

Elegant High-Performance Computing

Explore on GitHubRead Hacker News discussions
10Distinct people
14Mentions
0Explicit recommenders
518GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Elegant High-Performance Computing

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year14 dated mentions

Observed mentions: 2018-05-28 03:07:50 UTC to 2026-07-17 19:35:44 UTC.

012342018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments20182019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 2 mentions; 2 submissions, 0 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 4 mentions; 0 submissions, 4 comments20222023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC110
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC101
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC220
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC101
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC404
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC211
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC202
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC000
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC101

What Hacker News says

14 sources
heisig·Jun 22, 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...
In Connection Machine Lisp (1986)
heisig·Jun 22, 2024
Connection Machine Lisp never made it into production, but this paper had a profound impact on my scientific career. In particular, it was the following comment in the paper that triggered me to develop the Petalisp programming language (https://github.com/marcoheisig/Petalisp): > Nevertheless, we have implemented (on a single-processor system, the Symbolics 3600) an experimental version of Connection Machine Lisp with lazy xappings and have found it tremendously complicated to implement but useful in practice. I think there is a moral here: Don't hesitate to experiment with crazy ideas (lazy xappings), and don't be afraid to openly talk about those experiments. After eight years of development, I can definitely confirm that lazy arrays/xappings are tremendously complicated to implement but useful in practice :)
In Connection Machine Lisp (1986)
medo-bear·Nov 4, 2022
you shouldn't be attacked. i think its just that these type of questions do get asked a lot of time just to troll. i personally also think its a bad question because someone can give you a valid answer and you might not find the answer interesting. instead maybe it's better to ask something like: Q: is there an interesting 3D graphics software being developed in Lisp? A: https://github.com/kaveh808/kons-9 Q: is there an interesting GUI project being developed in Lisp? A: https://github.com/rabbibotton/clog Q: is there an interesting HPC project being developed in Lisp? A: https://github.com/marcoheisig/Petalisp Q: is there an interesting cryptography library being developed in Lisp? A: https://github.com/sharplispers/ironclad see how different these domains are? its unlikely someone will find more than one of these projects interesting
In Why Lisp?
medo-bear·Jul 21, 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 and it has a convolutional layer impl
Read the rest
emented https://github.com/marcoheisig/Petalisp https://github.com/marcoheisig/Petalisp/blob/master/examples...
In New Lisp-Stat Release
moonchild·Apr 8, 2022
Common lisp can be and has been successfully used for high-performance numerics[0] as well as low-level system code[1], all while being a high-level, garbage-collected language. (The latter property likely leading to better overall performance in large programs.) Verification may be interesting, for high-assurance (e.g. automotive/aerospace) applications. It's for this reason that a theorem prover[2] and an ml[3] have been implemented, and they interoperate freely with the rest of common lisp. In other words: we are already where you want to be, and we are even less stratified than you propose we would need to be. 0. https://github.com/marcoheisig/Petalisp 1. https://github.com/froggey/mezzano/ 2. https://www.cs.utexas.edu/users/moore/acl2/ 3. https://github.com/coalton-lang/coalton
In TypeScript as fast as Rust: TypeScript++
medo-bear·Oct 24, 2021
my background is also in physics and mathematics. in fact i think common lisp is well suited to a physicist-hacker type mindset. i could probably write a lengthy blog post about why i think this could be, but some of the key appeal factors are: * unmatched interactive development which could be used for exploration of problems such as dynamical and complex systems * although it is a very high level language you can perform low level optimizations in common lisp itself * extremely powerful macro system that allows you to develop in a declarative style to reflect your problem domain. for example you can program in a syntax that is natural to general or special relativity, quantum mechanics, etc. * fast and free implementations such as SBCL * the language is standardized so your code will not need to be unnecessarily maintained (this is good
Read the rest
for university professors that have repeating long-running courses) i am not sure what type of references you are asking for but i will list few open source projects that might be interesting to you: * CLASP - common lisp implementation build on top of LLVM. this project is headed by a computational chemist (prof. schafmeister) [0] * PetaLisp - for HPC [1] * MAGICL - Matrix Algebra proGrams In Common Lisp by Rigetti Computing [2] * Quil - quantum instruction language developed on top of common lisp [3] * CL-CUDA - use NVIDIA CUDA in Common Lisp programs [4] * MGL - Common Lisp machine learning library [5] * Maxima - a computer algebra system developed in common lisp [6] i think these are plenty to show that there exists serious interest in using common lisp for scientific computing. it is possible that some of the people behind these projects are on HN and maybe they can further expand on this [0] https://github.com/clasp-developers/clasp [1] https://github.com/marcoheisig/Petalisp [2] https://github.com/quil-lang/magicl [3] https://github.com/quil-lang/quilc [4] https://github.com/takagi/cl-cuda [5] https://github.com/melisgl/mgl also https://github.com/melisgl/higgsml [6] https://maxima.sourceforge.io/
In Idiomatic Clojure without sacrificing performance

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