High Performance Python

Princeton mini-course

By Henry Schreiner, with Jim Pivarski

Installation

To run on Binder, click this badge:

To install locally:

Download and install miniconda. On macOS with homebrew, just run brew cask install miniconda.

Run:

conda env create -f environment.yml

from this directory. This will create an environment performance-minicourse. To use:

conda activate performance-minicourse

And, to disable:

conda deactivate

or restart your terminal.

If you want to add a package, modify environment.yml then run:

conda env update -f environment.yml

Lessons

• 00 Intro: The introduction
• 01 Fractal accelerate: A look at a fractal computation, and ways to accelerate it with Numpy changes, numexpr, and numba.
• 02 Temperatures: A look at reading files and array manipulation in Numpy and Pandas.
• 03 MCMC: A Marco Chain Monte Carlo generator (and metropolis generator) in Python and Numba, with a focus on profiling.
• 04 Runge-Kutta: Implementing a popular integration algorithm in Numpy and Numba.
• 05 Distributed: An exploration of ways to break up code (fractal) into chunks for multithreading, multiproccessing, and Dask distribution.
• 06 Tensorflow: A look at implementing a Negative Log Likelihood function (used for unbinned fitting) in Numpy and Google's Tensorflow.
• 07 Callables: A look at Scipy's LowLevelCallable, and how to implement one with Numba.

Class participants: please complete the survey here.