From the course: Python Parallel and Concurrent Programming Part 2
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Speedup, latency, and throughput - Python Tutorial
From the course: Python Parallel and Concurrent Programming Part 2
Speedup, latency, and throughput
- There are several reasons for using multiple processors to execute a program in parallel. One reason, might be to increase the size of the problem you can tackle in a certain amount of time. For example, we're going to a party and I promise to bring 10 cupcakes. Working by myself, I can decorate 10 cupcakes in one hour, they're very fancy cupcakes, but if Baron joins as a second processor, doing the same type of work in parallel, together we can decorate 20 cupcakes in one hour. This type of parallelization is called Weak Scaling, we're keeping the size of the problem we're bringing in more processors to accomplish more work in the same amount of time. - Another reason for parallelization and bringing in more processors is to accomplish a given task faster. If Olivia promised to bring 10 cupcakes to the party, then working alone, it would take her one hour to decorate all of them but if we split the workload, so she'll do half and I'll do half then working together in parallel we…
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