Itamar Turner-Trauring - Optimize first, parallelize second: a better path to faster data processing

You’re processing a large amount of data with Python, and your code is too slow.
One obvious way to get faster results is adding multithreading or multiprocessing, so you can use multiple CPU cores.
Unfortunately, switching straight to parallelism is almost always premature, often unnecessary, and sometimes impossible.
We'll cover the different goals for performance, why parallelism only achieves one of them, the costs of parallelism, and the alternative: speeding up your code first.

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