Performance tuning in Python a talk by Jan Škoda

Python is a very efficient language, at least in terms of programmer’s time. With CPU time, it is much worse. Let’s talk about how to profile applications to find the slowest parts, what are the best ways to speed them up or what is the difference between minimizing latency and maximizing throughput.

We’ll take a look at tools like Cython and NumPy and think about the different ways to parallelize computation using threading, multiprocessing or asyncio. All those tools can be composed together to bypass common Python problems with GIL or inefficient memory management and provide surprising performance for compute intense tasks such as data processing.