pyperformance
pybench
| pyperformance | pybench | |
|---|---|---|
| 6 | 1 | |
| 817 | 10 | |
| 0.9% | - | |
| 6.6 | 0.0 | |
| 20 days ago | almost 2 years ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
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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.
pybench
What are some alternatives?
asv - Airspeed Velocity: A simple Python benchmarking tool with web-based reporting
ann-benchmarks - Benchmarks of approximate nearest neighbor libraries in Python
pyperf - Toolkit to run Python benchmarks
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.
tianshou - An elegant PyTorch deep reinforcement learning library.
ga-extractor - Tool for extracting Google Analytics data suitable for migrating to other platforms/databases
CARLA - CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
pyeventbus - Python Eventbus
Cython - The most widely used Python to C compiler
fashion-mnist - A MNIST-like fashion product database. Benchmark :point_down: