This lesson is still being designed and assembled (Pre-Alpha version)

This lesson is part of The Carpentries Incubator, a place to share and use each other's Carpentries-style lessons. This lesson has not been reviewed by and is not endorsed by The Carpentries.

Parallel Programming in Python

FIXME: home page introduction

Prerequisites

The participant should be:

familiar with basic Python: control flow, functions, numpy

comfortable working in Jupyter

Recommended:

understand how NumPy and/or Pandas work

Schedule

	Setup	Download files required for the lesson
00:00	1. Introduction	What problems are we solving, and what are we not discussing? Why do we use Python? What is parallel programming? Why can it be hard to write a parallel program?
00:25	2. Measuring performance	How do we know our program ran faster? How do we learn about efficiency?
01:25	3. Understanding parallelization in Python	What is the Global Interpreter Lock (GIL)? How do I parallelize a Python application? What is data parallelism? What is task parallelism? How do I use multiple threads in Python?
02:55	4. Dask abstractions: bags and delays	What abstractions does Dask offer? What programming patterns exist in the parallel universe?
04:25	5. Snakemake	What are computational workflows? How do you program using a build system? How do I mix Python code into a workflow?
05:15	6. Asyncio fundamentals	What is AsyncIO? How do I structure an async program?
06:15	7. Dynamic programming	How can I save intermediate result and recover from crashes? How can I prevent duplicate computations?
06:15	8.
06:15	Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.