Книга: Learning Concurrency in Python
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Understanding parallelism

In the first chapter, we covered a bit about Python's multiprocessing capabilities, and how we could use this to take advantage of more of the processing cores in our hardware. But what do we mean when we say that our programs are running in parallel?

Parallelism is the art of executing two or more actions simultaneously as opposed to concurrency in which you make progress on two or more things at the same time. This is an important distinction, and in order to achieve true parallelism, we'll need multiple processors on which to run our code at the same time.

A good analogy for parallel processing is to think of a queue for Coke. If you have, say, two queues of 20 people, all waiting to use a coke machine so that they can get through the rest of the day with a bit of a sugar rush, well, this would be an example of concurrency. Now say you were to introduce a second coke machine into the mix--this would then be an example of something happening in parallel. This is exactly how parallel processing works--each of the coke machines in that room represents one processing core, and is able to make progress on tasks simultaneously:

Source: https://github.com/montanaflynn/programming-articles/blob/master/articles/parallelism-and-concurrency-need-different-tools.md

A real-life example that highlights the true power of parallel processing is your computer's graphics card. These graphics cards tend to have hundreds, if not thousands, of individual processing cores that live independently, and can compute things at the same time. The reason we are able to run high-end PC games at such smooth frame rates is due to the fact we've been able to put so many parallel cores onto these cards.

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