I like SIMD, but before super-optimizing your code with SIMD and the like, really consider your data structures and access patterns.
I've been singing Data-Oriented Design's praises, so I'll just collect all my comments here [1], but I think it's a good approach to optimization. I played around with SIMD in my old code (in Zig), but my approach to modelling datastructures was so antithetical to optimization, it was like putting high-performance racing tires on a lemon with a broken engine.
It was the root-of-all-evil-type-premature-optimization, because I wasn't measuring performance, and I wasn't thinking about where the allocations were, etc. Now, I try to model my data as if it were SQL tables, see what my potential "primary keys" could be, and build my data structures around my access patterns.
For example, I used to model trees as structs pointing to other structs on the heap:
struct Tree {
tag: TreeTag,
children: Vec
}
Now my tree has all the bad characteristics of a linked list (*
n nodes *
m children), all the fragmentation of multiple heap vectors (*
n nodes), and terrible set-up / tear-down time (in this case, Drop alone was taking up a good chunk of runtime).
But a tree can be represented a million ways, and can always be linearized. So now I really consider my access/insert patterns of the tree, whether it's really a tree or some other sort of graph, whether I can store it in a Vec or a Struct of Vecs, etc. Since really looking at things through their access patterns and "primary keys", my code has been much faster and simpler.
This has the added effect that a lot of your data ends up in homogeneous arrays / vecs, which means that the compiler can do its SIMD magic, the CPU can read it from your L1 cache a million times faster, etc. And then when you need to drop down into SIMD yourself, you can write some awesome branchless code.
1. https://hn.algolia.com/?dateRange=all&page=0&prefix=true&que...