wkirgsn / poly-match Goto Github PK

The repo contains the source code for the Making Python 100x faster with less than 100 lines of Rust blog post (there's a copy in this repo as well).

It's a demo library in Python that we can converts parts of to Rust, to improve its performance.

Here's a table summarizing the perf gains:

There's also a "v1.5" version which is 6x faster, and uses "vectorizing" (doing more of the work directly in numpy). This version is much harder to optimize further (using Rust & technics shown in the post).

(*) v5 was added after the post was published, showing how to reduce Python to Rust overhead even more.

The code should work on all supported platforms (Python & Rust), but --native profiling is only supported by py-spy on x86 Linux and Windows.

(macOS / Arm Linux can still generate profiles viewing just the Python code.)

For example, to setup with conda, run:

conda install numpy pytest

Then install py-spy:

pip install py-spy

For the complete Rust setup, you'll need Rust (use rustup) and to also pip install maturin.

To build the native extension, run:

(cd poly_match_rs && maturin develop --release)

There are a few more optimizations we can try.

For example, we could use (dx^2 + dy^2) < dist^2 (calculating dist^2 only once, saving a sqrt). According to the profiler outputs, can we expect this to make a big difference?

We could build a Rust bench using criterion, which would require us to "split" the Python & pure-Rust parts (Maybe using AsRef<Polygon>).

We could also try to convert the list of Polygons only once to Rust at the start of the run. According to the profiler outputs, can we expect this to make a big difference? (See v5)

This repo is licensed under either of

Apache License, Version 2.0, (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
MIT license (LICENSE-MIT or http://opensource.org/licenses/MIT)

at your option.