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...
