stl-benchmark
jnumpy
| stl-benchmark | jnumpy | |
|---|---|---|
| 1 | 9 | |
| 6 | 227 | |
| - | 0.0% | |
| 0.0 | 3.9 | |
| over 1 year ago | 19 days ago | |
| C++ | Julia | |
| MIT License | 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.
stl-benchmark
-
JNumPy: Writing high-performance C extensions for Python in minutes
Some of these are just safety-by-default things. For example, IO in Julia is thread-safe by default, which I think is a good idea because safety-first programming is good for say throwing `print` into a threaded loop written in the REPL. Here for example, https://github.com/aaronang/stl-benchmark/pull/3, was a case where Julia saw a performance hit from C++ and I was curious and tracked it down to this locking-by-default behavior. I'm not sure of a better way of handling it: the C/C++ behavior of not locking by default would make doing things correctly simply would be very hard to use (and is very hard in those languages).
Though I agree parsers haven't gotten much love in Julia. That said, this repo is saying it's for implementing NumPy extensions, and I don't think NumPy has many parsers it's using.
jnumpy
- Making Python 100x faster with less than 100 lines of Rust
-
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?
makepackage - Package for easy packaging of Python code
ideas
poly-match - Source for the "Making Python 100x faster with less than 100 lines of Rust" blog post
PythonCall.jl - Python and Julia in harmony.
log-booster - An VS code extension to quickly add frequently used log statements
Schemathesis - Automate your API Testing: catch crashes, validate specs, and save time
PackageCompiler.jl - Compile your Julia Package
numexpr - Fast numerical array expression evaluator for Python, NumPy, Pandas, PyTables and more
scalene - Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals
truffleruby - A high performance implementation of the Ruby programming language, built on GraalVM.
jsmpeg - MPEG1 Video Decoder in JavaScript