Halide VS rayon

If CPU/GPU execution speed is the goal while simultaneously code golfing the source size, https://halide-lang.org/ might have come in handy.

This is a task where Halide https://halide-lang.org/ could really shine! It disconnects logic from scheduling (unrolling, vectorizing, tiling, caching intermediates etc), so every step the author describes in the article is a tunable in halide. halide doesn't appear to have bindings for golang so calling C++ from go might be the only viable option.

Probably would have been much easier to do 15 times in https://halide-lang.org/

The idea behind Halide is that scheduling memory access patterns is critical to performance. But, access patterns being interwoven into arithmetic algorithms makes them difficult to modify separately.

So, in Halide you specify the arithmetic and the schedule separately so you can rapidly iterate on either.

It is not the sorting per-se which was improved here, but sorting (particularly short sequences) on modern CPUs with really the complexity being on the difficulty of predicting what will work quickly on these modern CPUs.

Doing an empirical algorithm search to find which algorithms fit well on modern CPUs/memory systems is pretty common, see e.g. FFTW, ATLAS, https://halide-lang.org/

Halide https://halide-lang.org/

It doesn’t apply in this case, but in general if you really want the best vectorization I would suggest using https://halide-lang.org instead of trying to coerce your compiler.

If we drop the "APL" requirement, wouldn't Halide fit your criteria for the third?

Do some operations with .iter() then later use rayon to parallelize. So you can show how easy is to add a dependency and how easy is to parallelize.

rayon: Async CPU runtime for parallelism.

In the quest for more efficient solutions, the ONNX runtime emerged as a beacon of performance. The decision to transition from Typescript to Rust was an unconventional yet pivotal one. Driven by Rust's robust parallel processing capabilities using Rayon and seamless integration with ONNX through the ort crate, Repo-Query unlocked a realm of unparalleled efficiency. The result? A transformation from sluggish processing to, I have to say it, blazing-fast performance.

With a lot of rayon, blood, sweat and tears I finally managed to simulate a million buildings at 60fps :) Feel free to AMA, game is Combine And Conquer

(see https://github.com/rayon-rs/rayon/tree/master/src/iter/plumbing)

I did some very basic testing with this crate : https://crates.io/crates/rayon and it seems to work :

Sequentially, generating tree-sitter AST for each file and querying for the links of each file takes around 2.3 seconds. However, I randomly remembered this crate rayon, and I decided to test it. It ended up improving the performance (just by changing 2 lines of code) to 200-300ms by parallelizing the iterators and tree-sitter queries. MAJOR.

Now if you really want to use Rust, you can rewrite only the part that are slowing down your consumer. It's easy by using Py03 and maturin. Maybe also rayon to parallelize.