flamegraph VS rayon

You can install cargo-flamegraph with cargo install flamegraph. There are some underlying requirements to be able to use cargo-flamegraph; you will want to take a look at the repo here to make sure you have the right dependencies.

I tried to diagnose the issue with flamegraph, but unfortunately the flamegraph didn't show anything beyond the next call for some reason

having some tools to identify slowness origins (flamegraph is one... but not sure it's the way to go)

I'm currently implementing a solution for the first part of the day 16. It work but it is really slow... I'd like to : - understand why - having some tools to identify slowness origins (flamegraph is one... but not sure it's the way to go) - eventually have some clue/solution/idea - have general feedback on what in my "coding style" is not appropriate for rust (I come from java/kotlin/ts even if I've already coded a bit in c/c++) : for example I love iterator & sequence but i feel they are not really suited to overuse in rust (mostly because of async & result).

Use a profiler. Flamegraph is a good way to visualise profiler output. This lets you identify which functions are taking up a large amount of time - and hence helps you identify where to focus your optimisation efforts.

You tried trying to see what takes the most time under load via flames? https://github.com/flamegraph-rs/flamegraph

Well, in cases like that the answer is straight forward, use a profiler like https://github.com/flamegraph-rs/flamegraph

I don't see any obvious problems with your algorithm. I've had luck using cargo-flamegraph to identify the slow parts of my code. That's going to show you which parts to focus on improving the performance of!

From the readme of cargo flamegraph:

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.