For many applications PyPy can provide performance benefits right out of the box. However, little details can push your application to perform much better. In this tutorial we'll give you insights on how to push pypy to it's limites. We'll focus on understanding the performance characteristics of PyPy, and learning the analysis tools in order to maximize your applications performance.
We aim to teach people how to use performance tools available for PyPy as well as to understand PyPy's performance characteristics. We'll explain how different parts of PyPy interact (JIT, the garbage collector, the virtual machine runtime) and how to measure which part is eating your time. We'll give you a tour with jitviewer which is a crucial tool for understanding how your Python got compiled to assembler and whether it's performing well. We also plan to show potential pitfalls and usage patterns in the Python language that perform better or worse in the case of PyPy.
We'll also briefly mention how to get your application running on PyPy and how to avoid common pitfalls there, like reference counting or relying on C modules.
This tutorial is intended for people familiar with Python who have performance problems, no previous experience with PyPy is needed. We ask people to come with their own problems and we'll provide some example ones. Attendees should have the latest version of PyPy preinstalled on their laptops.
7th–15th March 2012