This is the first post in the series “Hunting Performance in Python Code”. Through each post I’ll present some of the tools and profilers that exists for Python code and how each of them helps you to better find bottlenecks both in frontend (Python scripts) and/or in the backend (Python interpreter).
Tag: PyPy
Enabling Profile Guided Optimizations for PyPy
PyPy, compared to CPython relies more on achieving speed-up by “jitting” code as often as possible, rather than rely on its interpreter. However, jitting is not always an option, or at least not entirely. A good improvement for CPython, that we think might benefit PyPy as well, without impacting the JIT performance is Profile Guided Optimization (PGO or profopt).
I thank the PyPy developer community for their patience, kind advice and constant feedback they gave me in #pypy IRC or through email, which helped me to make this possible, especially to Carl Friedrich Bolz-Tereick and Armin Rigo.
PyPy vs CPython Performance on OpenStack Neutron
This post contains details about a performance comparison between PyPy and CPython, when applied to OpenStack Neutron. The steps did to switch from CPython to PyPy are described in my post here.Read More »
Enabling PyPy as support for OpenStack Neutron
This is a guide to deploy OpenStack Neutron with PyPy support. The underlying purpose was to determine whether using PyPy instead of CPython works for the networking service, and whether we would notice any improvements and/or drawbacks. The performance results of this can be read in my post here.
Python Files 