Parallelization: Making the most of your Machine

By default, most programming languages like Python and R will only use one CPU core/thread to execute your code. For small workloads, this is often sufficient even on a personal device. However, larger data processing or analysis workflows will often taken far too long to run on a single core or thread.

Parallelization is the process of taking a large computational task and breaking it into smaller tasks that can all be run simultaneously and independently. For example, most bootstrap methods can be parallelized, since each bootstrap sample is processed independently. This page will introduce several tools and techniques for running parallel workflows.

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Title	Summary	Technical Level	Relevance
Parallel computing	A primer on the benefits of parallel computing	⭐⚫⚫⚫⚫	Almost all computing tasks
Terminal multiplexers	Use multiple terminal "windows" to run several workflows simultaneously	⭐⭐⚫⚫⚫	Small-scale server workflows
Parallelization in python	Introduces several python packages for parallel workflows	⭐⭐⭐⚫⚫	Python workflows
Parallelization in r	Introduces several R packages for parallel workflows	⭐⭐⭐⚫⚫	R workflows
Using SLURM to parallelize workflows	How to use the SLURM job scheduler to parallelize workflows	⭐⭐⭐⭐⚫	Large-scale server workflows