A performance analysis repository for small Python snippets.

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.

Benchmarks may vary across Python versions, for this reason we provide plots for the latest 3 stable versions:

Benchmarks run on the GitHub Actions runner ubuntu-latest, updated info about the hardware details of the runner available here.

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).

Slice VS List write in NumPy	Pseudo-Hankel matrix

• 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.

• Francesco Andreuzzi (CERN, SISSA) -- [email protected]