LoopVectorization.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 LoopVectori

Growing up, what my late father probably wanted most from me is for me to find a project of my own. When I was in high school, he once threatened me with "get a life, or I will get you one". Engines, and especially motorcycles, were always a passion of his. He grew up on a farm, and "was rebuilding tractor engines when the other kids were learning to ride bicycles." 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

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/ And then replacing the matmul.jl with the following: @avx for i = 1:m, j = 1:p z = 0.0 for k = 1:n z += a[i, k] * b[k, j] end out[i, j] = z end I get a 4x speedup from 2.72s to 0.63s. And with @avxt (threaded) using