CUDA.jl VS LoopVectorization.jl

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.

See https://juliagpu.org/

If you just want to do some numerical code that requires linear algebra and GPU, your best bet would be Julia or Python+JAX.

GitHub - JuliaGPU/CUDA.jl: CUDA programming in Julia.

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/

I'm excited to eventually see something like JuliaGPU with support for multiple backends.

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.

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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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/