Fluent Python, 2nd Edition

Chapter 19. Concurrency Models in Python

Concurrency is about dealing with lots of things at once.

Parallelism is about doing lots of things at once.

Not the same, but related.

One is about structure, one is about execution.

Concurrency provides a way to structure a solution to solve a problem that may (but not necessarily) be parallelizable.

Rob Pike, co-inventor of the Go language¹

This chapter is about how to make Python deal with “lots of things at once.” This may involve concurrent or parallel programming—even academics who are keen on jargon disagree on how to use those terms. I will adopt Rob Pike’s informal definitions in this chapter’s epigraph, but note that I’ve found papers and books that claim to be about parallel computing but are mostly about concurrency.²

Parallelism is a special case of concurrency, in Pike’s view. All parallel systems are concurrent, but not all concurrent systems are parallel. In the early 2000s we used single-core machines that handled 100 processes concurrently on GNU Linux. A modern laptop with 4 CPU cores is routinely running more than 200 processes at any given time under normal, casual use. To execute 200 tasks in parallel, you’d need 200 cores. So, in practice, most computing is concurrent and not parallel. The OS manages hundreds of processes, making sure each has an opportunity to make progress, even if the CPU itself can’t do more than four things at once.

This chapter assumes no prior knowledge of concurrent or parallel programming. ...