poly-match
jnumpy
poly-match | jnumpy | |
---|---|---|
6 | 9 | |
31 | 227 | |
- | 0.0% | |
2.3 | 3.9 | |
28 days ago | 23 days ago | |
Python | Julia | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
poly-match
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Improving Interoperability Between Rust and C++
Not my experience at all. At work we rewrote a small bit of hotspot python in Rust with no issues. This was what we primarily followed: https://ohadravid.github.io/posts/2023-03-rusty-python/
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How to convince my boss that Rust is usable
Take at look at this example, it still uses Python as an interface to Rust code. Maybe you can do something similar to still achieve performance improvements without changing the entire codebase.
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GDScript is fine
People are probably downvoting because it's needlessly hyperbolic and argumentative. Nobody is saying that python isn't faster to iterate with, but they're arguing that it would take months to get negligable performance gains in a lower level language, meanwhile here is a recent post from a company that increased the execution of they're python code by 100x with less than 100 lines of Rust. They also claim that nobody cares if something runs a few milliseconds faster, when we're talking about game dev, where games are frequently judged on how many milliseconds it takes to run game logic between frames.
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Making Python 100x faster with less than 100 lines of Rust
Semi Vectorized code:
https://github.com/ohadravid/poly-match/blob/main/poly_match...
Expecting Python engineers unable to read defacto standard numpy code but meanwhile expect everyone can read Rust.....
Not to mention that the semi-vectorized code is still suboptimal. Too many for loops despite the author clearly know they can all be vectorized.
For example instead the author can just write something like:
np.argmin(
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Blog Post: Making Python 100x faster with less than 100 lines of Rust
The article links to a full implementation, so you should be able to test this.
jnumpy
- Making Python 100x faster with less than 100 lines of Rust
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This Week in Python
jnumpy – Writing Python C extensions in Julia within 5 minutes
- GitHub - Suzhou-Tongyuan/jnumpy: Writing Python C extensions in Julia within 5 minutes.
- JNumPy: Writing high-performance C extensions for Python in minutes
What are some alternatives?
gopy - gopy generates a CPython extension module from a go package.
makepackage - Package for easy packaging of Python code
StaticCompiler.jl - Compiles Julia code to a standalone library (experimental)
ideas
truffleruby - A high performance implementation of the Ruby programming language, built on GraalVM.
PythonCall.jl - Python and Julia in harmony.
birthday-book-app - Rust in Anger: high-performance web applications
log-booster - An VS code extension to quickly add frequently used log statements
Schemathesis - Supercharge your API testing, catch bugs, and ensure compliance
numexpr - Fast numerical array expression evaluator for Python, NumPy, Pandas, PyTables and more
PackageCompiler.jl - Compile your Julia Package