Students will walk away with a high-level understanding of both parallel problems and how to reason about parallel computing frameworks. They will also walk away with hands-on experience using a variety of frameworks easily accessible from Python.

Knowledge of Python and general familiarity with the Jupyter notebook are assumed. This is generally aimed at a beginning to intermediate audience.

For the first half we cover basic ideas and common patterns in parallel computing, including embarrassingly parallel map, unstructured asynchronous submit, and large collections.

For the second half we cover complications arising from distributed memory computing and exercise the lessons learned in the first section by running informative examples on provided clusters.

• Part one
  • Parallel Map
  • Asynchronous Futures
  • High Level Datasets
• Part two
  • Processes and Threads. The GIL, inter-worker communication, and contention.
  • Distributed deployment
  • Cluster computing exercises

1. Install Anaconda

2. Update select packages

   Everyone:

    conda install -c conda-forge ipyparallel ujson dask distributed bokeh
    pip install snakeviz
   

   Python 2 users:

    conda install futures
   

   Linux/Mac users:

    conda install -c quasiben spark
   

Test your installation:

python -c 'import concurrent.futures, ipyparallel, dask, jupyter, pyspark'

We will generate a dataset for use locally. This will take up about 1GB of space in a new local directory, data/.

pip install fakestockdata
python prep.py

Google Compute Engine