# Parallel computing

Many calculations at once rather than one after another. Two scales on Eugo: across machines in a cluster, and inside a CPU core through vectorization.

Source: https://university.eugo.io/glossary/parallel-computing

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The two scales are worth keeping separate, because they are reached differently. Spreading work across machines is something you ask for, with the distribute decorator. Parallelism inside a core is something the runtime applies to your code without being asked, which is why the same loop can run faster after a rewrite that changed no logic.

## Related terms

- [Distributed computing](https://university.eugo.io/glossary/distributed-computing) — Splitting one job across several machines that work on it simultaneously. The core technique of HPC, and what the distribute decorator gives you in Python.
- [Vectorization](https://university.eugo.io/glossary/vectorization) — Rewriting operations so a single instruction processes several data elements at once, using the processor’s wide registers.
- [High-performance computing (HPC)](https://university.eugo.io/glossary/high-performance-computing) — Running work across many machines at once so it finishes far sooner. The technique behind weather models, genomics, and large-scale simulation.