https://github.com/fandreuz/python-performance-playground
Performance analysis of Python snippets for scientific computing
https://github.com/fandreuz/python-performance-playground
dask high-performance-computing numba numpy performance-analysis python
Last synced: 7 months ago
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Performance analysis of Python snippets for scientific computing
- Host: GitHub
- URL: https://github.com/fandreuz/python-performance-playground
- Owner: fandreuz
- License: mit
- Created: 2022-07-23T23:31:28.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2022-10-17T00:58:31.000Z (over 3 years ago)
- Last Synced: 2023-06-03T14:29:24.761Z (over 2 years ago)
- Topics: dask, high-performance-computing, numba, numpy, performance-analysis, python
- Language: Python
- Homepage:
- Size: 25.4 MB
- Stars: 4
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
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# python-performance-playground
A performance analysis repository for small Python snippets.
## Goal
Ever felt the need to test multiple methods to solve the same, self-contained,
easily-explainable Python problem, in order to find the most performant one?
Me too, but I'm always too lazy to generalize the one/two lines I wrote, prepare
a function and run the experiments over a meaningful set of inputs.
I've collected some of my snippets over a few months, and packed them nicely
into a repository, along with an accomodating GitHub action which cares about
running the experiments and doing the plots.
### Software and hardware
Benchmarks may vary across Python versions, for this reason we provide plots
for the latest 3 stable versions:
| **Python version** | **Branch** |
|:---|:---|
| `3.10` | [`master`](https://github.com/fAndreuzzi/python-performance-playground/) |
| `3.9` | [`python-3.9`](https://github.com/fAndreuzzi/python-performance-playground/tree/python-3.9) |
| `3.8` | [`python-3.8`](https://github.com/fAndreuzzi/python-performance-playground/tree/python-3.8) |
Benchmarks run on the GitHub Actions runner `ubuntu-latest`, updated info about
the hardware details of the runner available
[here](https://docs.github.com/en/actions/using-github-hosted-runners/about-github-hosted-runners#supported-runners-and-hardware-resources).
### Content
| **Directory** | **Content** |
|:---|:---|
| [`./python/`](python/) | General snippets in pure Python |
| [`./numpy/`](numpy/) | Comparisons among multiple equivalent NumPy (or Python) snippets |
| [`./dask/`](dask/) | Benchmarks of equivalent Dask snippets. |
## Contributing
Snippets welcome! Just prepare a PR following the standard format in the
repository:
- Find the right place for your snippet (e.g. `numpy`, `python`, `dask`, ...)
- Find an appropriate name for your snippet
- Provide an `.ipynb` files which runs the experiments (please use the
annotations `@kernel, @data, ...` like we did for the other snippets)
- Let the bot do its work (after your PR is merged).
## Examples
| Slice VS List write in NumPy | Pseudo-Hankel matrix |
| -------------------------------------------------------------- | -------------------------------------------------------------- |
| [](numpy/write-in-slices) | [](numpy/hankel-matrix) |
## Disclaimer
- Benchmarks in this repository are usually very simple, use dummy data and are
in general dried of any meaning except for the pursue for performance.
- Benchmarks in this repository are assumed to be valid only for common
use-cases, while industrial or scientific applications might suffer of
asymptotic pathological patterns which deserve a customized treatment.
- I do not claim in any way that benchmarks in this repository are enough to
provide a full view of the performance of the methods treated. For instance
I do not probe memory access or caching with tailored data to stress the
computer resources. Again, my aim is to see what happens with common
use-cases.
## Contributors
- Francesco Andreuzzi (CERN, SISSA) -- andreuzzi.francesco@gmail.com