julia-vim VS LoopVectorization.jl

There's a handy Vim plug-in for Julia that will convert latex commands to symbols so it's a one character difference for a pretty notable improvement to readability when you start to get into longer equations.

Nowadays during my master thesis does lots of equations appear in my python code, but I would love to use tex rendering in some way, like latex preview in emacs. However, I know that there is some great latex rendering such as tex-conceal.vim and latex_to_unicode in julia-vim, but I am not able to make it work for python comments. Any idea on how to solve this?

There is a plugin I tried which is called julia-vim. However, this plugin is too broken for me. It conflicts with other completion plugins which makes it so hard to either fix or manage my configuration and keymaps. Fortunately, I found cmp-latex-symbols, a completion plugin that as described in the README

I use Git and plain text files. The Julia addon for Vim allows one to write UTF-8 math symbols with LaTeX commands.

I use julia.vim for unicode support. I find it a bit more responsive than agda-vim, and it has more symbols (the list is autogenerated). but I have two gripes with it:

Ideally, yes, I want to use LSP in order for it to work as close as possible from my nvim with julia-vim, coc.nvim and vim-julia-cell. At least until I'm more familiarized with Emacs. I really do want to learn, but I cannot just stop my daily work, so the best world possible would be to be able to keep working while learning.

I use vim+vim_slime along with the julia plugin configured with tab latex to unicode conversion. This means I can have a REPL open in a split vim buffer and send chunks from my script directly to the REPL. It's really lightweight and fast. I'm working to make some functional snippets too.

Since I was still missing some more advanced Vim features, I did some search and now have a pretty decent terminal based environment. The base is Tmux + Neovim- I open 2 panes, where one is used for coding and the other is Julia REPL. I use julia-vim plugin for base syntax and code highlights and vim-slime with vim-julia-cell for live sending of the code from the Neovim to the REPL.

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/