Parallelization: Making the most of your Machine: Difference between revisions
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| | |How to use the SLURM job scheduler to parallelize workflows | ||
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Revision as of 03:33, 27 March 2026
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.
| Title | Summary | Technical Level | Scope |
|---|---|---|---|
| Parallel computing | A primer on the benefits of parallel computing | ⭐⚫⚫⚫⚫ | Everything |
| Terminal multiplexers | Use multiple terminal "windows" to run several workflows simultaneously | ⭐⭐⚫⚫⚫ | Any machine that can run Bash commands |
| Parallelization in python | Introduces several python packages for parallel workflows | ⭐⭐⭐⚫⚫ | Any machine that runs Python |
| Parallelization in r | Introduces several R packages for parallel workflows | ⭐⭐⭐⚫⚫ | Any machine that runs R |
| Using SLURM to parallelize workflows | How to use the SLURM job scheduler to parallelize workflows | ⭐⭐⭐⭐⚫ | Any server that uses SLURM |
