Multitasking

In a mind-blowing twist, in computer science concurrent and simultaneous aren't synonyms.

Concurrency denotes access to a shared resource within a time frame. And there are different approaches to achieving this.

To illustrate the difference lets imagine we're making breakfast, and we want eggs and toast with a glass of fresh orange juice.

In Sequence

If we did it in a linear way we might take 2 minutes to cook scrambled eggs; once they're ready we would pop bread in the toaster, wait another two minutes, and spend an extra 30 seconds spreading butter on our toast at the end. Finally we'd move on to the juicer and spend one minute pressing oranges. Total preparation time 5m:30s

Eggs are cold by then!

With multitasking

To solve the cold eggs problem we can use a multitasking approach instead of waiting for each part to complete in turn.

If we cook multithreaded style, we may crack the eggs open on a pan, then turn to the toaster and put the bread in. We then go back and forth between the juicer and our eggs, making sure eggs don't stick to the bottom of the pan, and squeezing oranges.

One minute in we get our juice. Two minutes in, eggs are ready and bread pops out. We spend the same 30 seconds as before applying golden buttery goodness.

In reality all that context switching between juicer, pan, and toaster isn't instant, so let's add 30 seconds more to account for the time it takes to move from one to another. Total preparation time 3m:00s! We saved a whole two minutes over the linear method.

If we did it in parallel, we'd ask somebody (let's say a couple of friends) to help out. We could cook the eggs ourselves, while one of them does the toast and the other prepares the juice. Total time goes down to 2m:30s, which was the time of the slowest task.


This distinction becomes important to understand when choosing a multiprocessing model, as each offer advantages and tradeoffs.