Async Techniques and Examples in Python Transcripts
Chapter: Leveraging CPU cores with multiprocessing
Lecture: Python async landscape: multiprocessing

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0:00 So far we been primarily focused on using parallelism and while we're waiting on external services or doing other things like that.
0:09 We're not trying to take advantage of the processors that have all these cores. We're just trying to make our computers do more
0:15 typically talking to external systems. And that's great, that's a really, really common probably the most common reason people use parallelism.
0:22 But with these modern CPUs, you probably do want to take full advantage of their computational power.
0:28 So that's what we're going to focus on in this chapter. That means we've jumped our divider line. We're no longer in the do more at once section.
0:35 We're now trying to do things faster. As in, run a particular algorithm faster on a given piece of hardware
0:41 especially multicore, multiCPU type of machines. So we're going to focus on multiprocessing a particular module which saw just a little bit of a cameo
0:51 in the previous chapter. Multiprocessing is very similar to the threading programming model except we'll see that with multiprocessing
0:59 each operation runs in an entirely separate Python process.

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