Parallel Python: Analyzing Large Datasets
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 will cover basic ideas and common patterns encountered when analyzing large data sets in parallel. We start by diving into a sequence of examples that require increasingly complex tools. From the most basic parallel API: map, we will cover some general asynchronous programming with Futures, and high level APIs for large data sets, such as Spark RDDs and Dask collections, and streaming patterns. For the second half, we focus on traits of particular parallel frameworks, including strategies for picking the right tool for your job. We will finish with some common challenges in parallel analysis, such as debugging parallel code when it goes wrong, as well as deployment and setup strategies.
- Part one
- Parallel Map
- Asynchronous Futures
- High Level Datasets
- Part two
- Processes and Threads. The GIL, inter-worker communication, and contention.
- Latency and overhead. Batching, profiling.
- Communication mechanisms. Sockets, MPI, Disk, IPC.
- Stuff that gets in the way. Serialization, Native vs JVM, Setup, Resource Managers, Sample Configurations
- Debugging async and parallel code / Historical perspective
- Install Anaconda
Update select packages
conda install -c conda-forge ipyparallel ujson pip install snakeviz
Python 2 users:
conda install futures
conda install -c quasiben spark
Test your installation:
python -c 'import concurrent.futures, ipyparallel, dask, jupyter, pyspark'