pyperformance
Cython
pyperformance | Cython | |
---|---|---|
6 | 79 | |
817 | 8,912 | |
0.9% | 1.0% | |
6.6 | 9.8 | |
20 days ago | 7 days ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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.
pyperformance
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Phoronix: PyPerformance benchmark is on average 32% faster on Python 3.11 compared to 3.10 (on a Ryzen 9 5950X)
PyPerformance benchmark: https://github.com/python/pyperformance
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Faster CPython 3.12 Plan
25% number is from pyperformance benchmark suite, which you can replicate. Whether pyperformance is representative benchmark suite is another question.
https://github.com/python/pyperformance
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The Performance Benchmarks Comparing various combinations of GCC and Python
For each combination, We launch a GCC container and build Python with the GCC. Then run benchmarks using pyperformance and export to a JSON file.
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This Week In Python
pyperformance – Python Performance Benchmark Suite
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Hello, I created a interpreted dynamic programming language in C#. I use a bytecode compiler and a vm for interpretation. Right now I'm trying to optimise it. Any help would be great!
There are some standard benchmarks like fannkuch, deltablue, and so on (see a bunch for Python here) that you can port to your VM. They have adjustable values that you can raise or lower to increase or decrease the amount of time you take.
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Why is python so much slower on MacOS?
So I decided to run some actual benchmark suite. I found pyperformance which would seem to do the trick.
Cython
- Ask HN: C/C++ developer wanting to learn efficient Python
- Ask HN: Is there a way to use Python statically typed or with any type-checking?
- Cython 3.0
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How to make a c++ python extension?
The approach that I favour is to use Cython. The nice thing with this approach is that your code is still written as (almost) Python, but so long as you define all required types correctly it will automatically create the C extension for you. Early versions of Cython required using Cython specific typing (Python didn't have type hints when Cython was created), but it can now use Python's type hints.
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Never again
and again, everything that was released after using an older version of cython.
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Codon: Python Compiler
Just for reference,
* Nuitka[0] "is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11."
* Pypy[1] "is a replacement for CPython" with builtin optimizations such as on the fly JIT compiles.
* Cython[2] "is an optimising static compiler for both the Python programming language and the extended Cython programming language... makes writing C extensions for Python as easy as Python itself."
* Numba[3] "is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code."
* Pyston[4] "is a performance-optimizing JIT for Python, and is drop-in compatible with ... CPython 3.8.12"
[0] https://github.com/Nuitka/Nuitka
[1] https://www.pypy.org/
[2] https://cython.org/
[3] https://numba.pydata.org/
[4] https://github.com/pyston/pyston
- Slow Rust Compiler is a Feature, not a Bug.
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Any faster Python alternatives?
Profile and optimize the hotspots with cython (or whatever the cool kids are using these days... It's been a while.)
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What exactly is 'JIT'?
JIT essentially means generating machine code for the language on the fly, either during loading of the interpreter (method JIT), or by profiling and optimizing hotspots (tracing JIT). The language itself can be statically or dynamically typed. You could also compile a dynamic language ahead of time, for example, cython.
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Python executable makers
Cython - - embed demo
What are some alternatives?
pybench - Python benchmark tool inspired by Geekbench.
SWIG - SWIG is a software development tool that connects programs written in C and C++ with a variety of high-level programming languages.
asv - Airspeed Velocity: A simple Python benchmarking tool with web-based reporting
PyPy
pyperf - Toolkit to run Python benchmarks
mypyc - Compile type annotated Python to fast C extensions
Nuitka - Nuitka is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11. You feed it your Python app, it does a lot of clever things, and spits out an executable or extension module.
Pyston - A faster and highly-compatible implementation of the Python programming language.
ga-extractor - Tool for extracting Google Analytics data suitable for migrating to other platforms/databases
Pyjion
pyeventbus - Python Eventbus
Stackless Python