CUDA.jl
awesome-lisp-companies
CUDA.jl | awesome-lisp-companies | |
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15 | 51 | |
1,133 | 577 | |
1.1% | - | |
9.5 | 6.8 | |
7 days ago | about 1 month ago | |
Julia | ||
GNU General Public License v3.0 or later | - |
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.
CUDA.jl
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Ask HN: Best way to learn GPU programming?
It would also mean learning Julia, but you can write GPU kernels in Julia and then compile for NVidia CUDA, AMD ROCm or IBM oneAPI.
https://juliagpu.org/
I've written CUDA kernels and I knew nothing about it going in.
- What's your main programming language?
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How is Julia Performance with GPUs (for LLMs)?
See https://juliagpu.org/
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Yann Lecun: ML would have advanced if other lang had been adopted versus Python
If you look at Julia open source projects you'll see that the projects tend to have a lot more contributors than the Python counterparts, even over smaller time periods. A package for defining statistical distributions has had 202 contributors (https://github.com/JuliaStats/Distributions.jl), etc. Julia Base even has had over 1,300 contributors (https://github.com/JuliaLang/julia) which is quite a lot for a core language, and that's mostly because the majority of the core is in Julia itself.
This is one of the things that was noted quite a bit at this SIAM CSE conference, that Julia development tends to have a lot more code reuse than other ecosystems like Python. For example, the various machine learning libraries like Flux.jl and Lux.jl share a lot of layer intrinsics in NNlib.jl (https://github.com/FluxML/NNlib.jl), the same GPU libraries (https://github.com/JuliaGPU/CUDA.jl), the same automatic differentiation library (https://github.com/FluxML/Zygote.jl), and of course the same JIT compiler (Julia itself). These two libraries are far enough apart that people say "Flux is to PyTorch as Lux is to JAX/flax", but while in the Python world those share almost 0 code or implementation, in the Julia world they share >90% of the core internals but have different higher levels APIs.
If one hasn't participated in this space it's a bit hard to fathom how much code reuse goes on and how that is influenced by the design of multiple dispatch. This is one of the reasons there is so much cohesion in the community since it doesn't matter if one person is an ecologist and the other is a financial engineer, you may both be contributing to the same library like Distances.jl just adding a distance function which is then used in thousands of places. With the Python ecosystem you tend to have a lot more "megapackages", PyTorch, SciPy, etc. where the barrier to entry is generally a lot higher (and sometimes requires handling the build systems, fun times). But in the Julia ecosystem you have a lot of core development happening in "small" but central libraries, like Distances.jl or Distributions.jl, which are simple enough for an undergrad to get productive in a week but is then used everywhere (Distributions.jl for example is used in every statistics package, and definitions of prior distributions for Turing.jl's probabilistic programming language, etc.).
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C++ is making me depressed / CUDA question
If you just want to do some numerical code that requires linear algebra and GPU, your best bet would be Julia or Python+JAX.
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Parallélisation distribuée presque triviale d’applications GPU et CPU basées sur des Stencils avec…
GitHub - JuliaGPU/CUDA.jl: CUDA programming in Julia.
- Why Fortran is easy to learn
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Generic GPU Kernels
Should have (2017) in the title.
Indeed cool to program julia directly on the GPU and Julia on GPU and this has further evolved since then, see https://juliagpu.org/
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Announcing The Rust CUDA Project; An ecosystem of crates and tools for writing and executing extremely fast GPU code fully in Rust
I'm excited to eventually see something like JuliaGPU with support for multiple backends.
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[Media] 100% Rust path tracer running on CPU, GPU (CUDA), and OptiX (for denoising) using one of my upcoming projects. There is no C/C++ code at all, the program shares a single rust crate for the core raytracer and uses rust for the viewer and renderer.
That's really cool! Have you looked at CUDA.jl for the Julia language? Maybe you could take some ideas from there. I am pretty sure it does the same thing you do here, and they support any arbitrary code with the limitations that you cannot allocate memory, I/O is disallowed, and badly-typed code(dynamic) will not compile.
awesome-lisp-companies
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Google Common Lisp Style Guide
Thanks to ITA Software (powering Kayak and Orbitz), Google dedicates resources to open-source Common Lisp development. More specifically, to SBCL:
> Doug Katzman talked about his work at Google getting SBCL to work with Unix better. For those of you who don’t know, he’s done a lot of work on SBCL over the past couple of years, not only adding a lot of new features to the GC and making it play better with applications which have alien parts to them, but also has done a tremendous amount of cleanup on the internals and has helped SBCL become even more Sanely Bootstrappable. That’s a topic for another time, and I hope Doug or Christophe will have the time to write up about the recent improvements to the process, since it really is quite interesting.
> Anyway, what Doug talked about was his work on making SBCL more amenable to external debugging tools, such as gdb and external profilers. It seems like they interface with aliens a lot from Lisp at Google, so it’s nice to have backtraces from alien tools understand Lisp. It turns out a lot of prerequisite work was needed to make SBCL play nice like this, including implementing a non-moving GC runtime, so that Lisp objects and especially Lisp code (which are normally dynamic space objects and move around just like everything else) can’t evade the aliens and will always have known locations.
https://mstmetent.blogspot.com/2020/01/sbcl20-in-vienna-last...
https://lisp-journey.gitlab.io/blog/yes-google-develops-comm...
The ASDF system definition facility, at the heart of CL projects, also comes from Google developers.
While we're at it, some more companies using CL today: https://github.com/azzamsa/awesome-lisp-companies/
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Why Is Common Lisp Not the Most Popular Programming Language?
Everyone, if you don't have a clue on how's Common Lisp going these days, I suggest:
https://lisp-journey.gitlab.io/blog/these-years-in-common-li... (https://www.reddit.com/r/lisp/comments/107oejk/these_years_i...)
A curated list of libraries: https://github.com/CodyReichert/awesome-cl
Some companies, the ones we hear about: https://github.com/azzamsa/awesome-lisp-companies/
and oh, some more editors besides Emacs or Vim: https://lispcookbook.github.io/cl-cookbook/editor-support.ht... (Atom/Pulsar support is good, VSCode support less so, Jetbrains one getting good, Lem is a modern Emacsy built in CL, Jupyter notebooks, cl-repl for a terminal REPL, etc)
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We need to talk about parentheses
Examples (for Common Lisp, so not citing Emacs): reddit v1, Google's ITA Software that powers airfare search engines (Kayak, Orbitz…), Postgres' pgloader (http://pgloader.io/), which was re-written from Python to Common Lisp, Opus Modus for music composition, the Maxima CAS, PTC 3D designer CAD software (used by big brands worldwide), Grammarly, Mirai, the 3D editor that designed Gollum's face, the ScoreCloud app that lets you whistle or play an instrument and get the music score,
but also the ACL2 theorem prover, used in the industry since the 90s, NASA's PVS provers and SPIKE scheduler used for Hubble and JWT, many companies in Quantum Computing, companies like SISCOG, who plans the transportation systems of european metropolis' underground since the 80s, Ravenpack who's into big-data analysis for financial services (they might be hiring), Keepit (https://www.keepit.com/), Pocket Change (Japan, https://www.pocket-change.jp/en/), the new Feetr in trading (https://feetr.io/, you can search HN), Airbus, Alstom, Planisware (https://planisware.com),
or also the open-source screenshotbot (https://screenshotbot.io), the Kandria game (https://kandria.com/),
and the companies in https://github.com/azzamsa/awesome-lisp-companies and on LispWorks and Allegro's Success Stories.
https://github.com/tamurashingo/reddit1.0/
http://opusmodus.com/
https://www.ptc.com/en/products/cad/3d-design
http://www.izware.com/mirai
https://apps.apple.com/us/app/scorecloud-express/id566535238
- A Tour of Lisps
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All of Mark Watson's Lisp Books
> but there doesn't seem to be one that really stands out as pragmatic, industrial
disagree ;) This industrial language is Common Lisp.
Some industrial uses:
- http://www.lispworks.com/success-stories/index.html
- https://github.com/azzamsa/awesome-lisp-companies/
- https://lisp-lang.org/success/
Example companies: Intel's programmable chips, the ACL2 theorem prover (https://royalsocietypublishing.org/doi/10.1098/rsta.2015.039...), urban transportation planning systems (SISCOG), Quantum Computing (HRL Labs, Rigetti…), big data financial analysis (Ravenpack, they might be hiring), Google, Boeing, the NASA, etc.
ps: Python competing? strong disagree^^
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Why Common Lisp is used to implement commercial products at Secure Outcomes (2010)
and of course, a quite recent list of companies, in addition of LW's success stories page: https://github.com/azzamsa/awesome-lisp-companies/
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Steel Bank Common Lisp
Hey there, newer member of the first group here. Please see https://github.com/azzamsa/awesome-lisp-companies/ to update your meta-comment. So, is CL used in the industry today, yes or no?
Personal note: I much prefer to maintain a long-living software in Common Lisp rather than in Python, thank you very much. May all the new programmers learn easily and all the teams have lots of ~~burden~~ work with Python, good for them.
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Racket: The Lisp for the Modern Day
Common Lisp has many industrial uses though.
(https://github.com/azzamsa/awesome-lisp-companies/
https://lisp-lang.org/success/
http://www.lispworks.com/success-stories/index.html
such as
https://www.cs.utexas.edu/users/moore/acl2/ (theorem prover used by big corp©)
https://allegrograph.com/press_room/barefoot-networks-uses-f... (Intel programmable chip)
quantum compilers https://news.ycombinator.com/item?id=32741928
etc, etc, etc)
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Why Lisp Syntax Works
A few more that we know of, using CL today: https://github.com/azzamsa/awesome-lisp-companies/
Others: https://lisp-lang.org/success/
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How to Understand and Use Common Lisp
yes
https://github.com/azzamsa/awesome-lisp-companies
http://lisp-lang.org/success/
industrial theorem prover, design of Intel chips, quantum compilers...
and little me, being more productive and having more fun than with python to deploy boring tools (read a DB, format the data, send to FTP servers, show a web interface...).
What are some alternatives?
LoopVectorization.jl - Macro(s) for vectorizing loops.
Carp - A statically typed lisp, without a GC, for real-time applications.
cunumeric - An Aspiring Drop-In Replacement for NumPy at Scale
portacle - A portable common lisp development environment
awesome-quant - A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
julia - The Julia Programming Language
cudf - cuDF - GPU DataFrame Library
coalton - Coalton is an efficient, statically typed functional programming language that supercharges Common Lisp.
Tullio.jl - ⅀
Fennel - Lua Lisp Language
GPUCompiler.jl - Reusable compiler infrastructure for Julia GPU backends.
kandria - A post-apocalyptic actionRPG. Now on Steam!