LoopVectorization.jl VS awesome-lisp-companies

It is a little disappointing that they're setting the bar against vanilla Python in their comparisons. While I'm sure they have put massive engineering effort into their ML compiler, the demos they showed of matmul are not that impressive in an absolute sense; with the analogous Julia code, making use of [LoopVectorization.jl](https://github.com/JuliaSIMD/LoopVectorization.jl) to automatically choose good defaults for vectorization, etc...

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> My mistake in retrospect was using small arrays as part of a struct, which being immutable got replaced at each time step with a new struct requiring new arrays to be allocated and initialized. I would not have done that in c++, but julia puts my brain in matlab mode.

I see. Yes, it's an interesting design space where Julia makes both heap and stack allocations easy enough, so sometimes you just reach for the heap like in MATLAB mode. Hopefully Prem and Shuhei's work lands soon enough to stack allocate small non-escaping arrays so that user's done need to think about this.

> Alignment I'd assumed, but padding the struct instead of the tuple did nothing, so probably extra work to clear a piece of an simd load. Any insight on why avx availability didn't help would be appreciated. I did verify some avx instructions were in the asm it generated, so it knew, it just didn't use.

The major differences at this point seem to come down to GCC (g++) vs LLVM and proofs of aliasing. LLVM's auto-vectorizer isn't that great, and it seems to be able to prove 2 arrays are not aliasing less reliably. For the first part, some people have just improved the loop analysis code from the Julia side (https://github.com/JuliaSIMD/LoopVectorization.jl), forcing SIMD onto LLVM can help it make the right choices. But for the second part you do need to do `@simd ivdep for ...` (or use LoopVectorization.jl) to match some C++ examples. This is hopefully one of the things that the JET.jl and other new analysis passes can help with, along with the new effects system (see https://github.com/JuliaLang/julia/pull/43852, this is a pretty huge new compiler feature in v1.8, but right now it's manually specified and will take time before things like https://github.com/JuliaLang/julia/pull/44822 land and start to make it more pervasive). When that's all together, LLVM will have more ammo for proving things more effectively (pun intended).

This looks nice too. Seems to be maintained and it even has a vmap-function. What more can one ask for ;) https://github.com/JuliaSIMD/LoopVectorization.jl

Have you checked out https://github.com/JuliaSIMD/LoopVectorization.jl ? It may be useful for your specific use case

And the "how" behind Octavian.jl is basically LoopVectorization.jl [1], which helps make optimal use of your CPU's SIMD instructions.

Currently there can some nontrivial compilation latency with this approach, but since LV ultimately emits custom LLVM it's actually perfectly compatible with StaticCompiler.jl [2] following Mason's rewrite, so stay tuned on that front.

[1] https://github.com/JuliaSIMD/LoopVectorization.jl

[2] https://github.com/tshort/StaticCompiler.jl

Yes, and sorry if I also came off as combative here, it was not my intention either. I've used some Common Lisp before I got into Julia (though I never got super proficient with it) and I think it's an excellent language and it's too bad it doesn't get more attention.

I just wanted to share what I think is cool about julia from a metaprogramming point of view, which I think is actually its greatest strength.

> here is a hypothetical question that can be asked: would a julia programmer be more powerful if llvm was written in julia? i think the answer is clear that they would be

Sure, I'd agree it'd be great if LLVM was written in julia. However, I also don't think it's a very high priority because there are all sorts of ways to basically slap LLVM's hands out of the way and say "no, I'll just do this part myself."

E.g. consider LoopVectorization.jl [1] which is doing some very advanced program transformations that would normally be done at the LLVM (or lower) level. This package is written in pure Julia and is all about bypassing LLVM's pipelines and creating hyper efficient microkernels that are competitive with the handwritten assembly in BLAS systems.

To your point, yes Chris' life likely would have been easier here if LLVM was written in julia, but also he managed to create this with a lot less man-power in a lot less time than anything like it that I know of, and it's screaming fast so I don't think it was such a huge impediment for him that LLVM wasn't implemented in julia.

[1] https://github.com/JuliaSIMD/LoopVectorization.jl

He still holds a few land speed records he set with motorcycles he designed and built.

But I had no real hobbies or passions of my own, other than playing card games.

It wasn't until my twenties, after I already graduated college with degrees I wasn't interested in and my dad's health failed, that I first tried programming. A decade earlier, my dad was attending the local Linux meetings when away from his machine shop.

Programming, and especially performance optimization/loop vectorization are now my passion and consume most of my free time (https://github.com/JuliaSIMD/LoopVectorization.jl).

Hearing all the stories about people starting and getting hooked when they were 11 makes me feel like I lost a dozen years of my life. I had every opportunity, but just didn't take them. If I had children, I would worry for them.

Julia will often do SIMD under the hood with either a for loop or a broadcasted version, so you generally shouldn't have to worry about it. But for more advanced cases you can look at https://github.com/JuliaSIMD/LoopVectorization.jl

Very often benchmarks include compilation time of julia, which might be slow. Sometimes they rightfully do so, but often it's really apples and oranges when benchmarking vs C/C++/Rust/Fortran. Julia 1.6 shows compilation time in the `@time f()` macro, but Julia programmers typically use @btime from the BenchmarkTools package to get better timings (e.g. median runtime over n function calls).

I think it's more interesting to see what people do with the language instead of focusing on microbenchmarks. There's for instance this great package https://github.com/JuliaSIMD/LoopVectorization.jl which exports a simple macro `@avx` which you can stick to loops to vectorize them in ways better than the compiler (=LLVM). It's quite remarkable you can implement this in the language as a package as opposed to having LLVM improve or the julia compiler team figure this out.

See the docs which kinda read like blog posts: https://juliasimd.github.io/LoopVectorization.jl/stable/

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/

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)

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

> 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^^

and of course, a quite recent list of companies, in addition of LW's success stories page: https://github.com/azzamsa/awesome-lisp-companies/

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.

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)

A few more that we know of, using CL today: https://github.com/azzamsa/awesome-lisp-companies/

Others: https://lisp-lang.org/success/

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...).