Programming Python, 4th Edition

Chapter 5. Parallel System Tools

“Telling the Monkeys What to Do”

Most computers spend a lot of time doing nothing. If you start a system monitor tool and watch the CPU utilization, you’ll see what I mean—it’s rare to see one hit 100 percent, even when you are running multiple programs.^[12] There are just too many delays built into software: disk accesses, network traffic, database queries, waiting for users to click a button, and so on. In fact, the majority of a modern CPU’s capacity is often spent in an idle state; faster chips help speed up performance demand peaks, but much of their power can go largely unused.

Early on in computing, programmers realized that they could tap into such unused processing power by running more than one program at the same time. By dividing the CPU’s attention among a set of tasks, its capacity need not go to waste while any given task is waiting for an external event to occur. The technique is usually called parallel processing (and sometimes “multiprocessing” or even “multitasking”) because many tasks seem to be performed at once, overlapping and parallel in time. It’s at the heart of modern operating systems, and it gave rise to the notion of multiple-active-window computer interfaces we’ve all come to take for granted. Even within a single program, dividing processing into tasks that run in parallel can make the overall system faster, at least as measured by the clock on your wall.

Just as important is that modern software systems are expected ...