Skip to content

BENCHMARKS.md

Latest commit

 

History

History
43 lines (30 loc) · 3.18 KB

BENCHMARKS.md

File metadata and controls

43 lines (30 loc) · 3.18 KB
Raw

python-build-standalone performance

Shows a chart with violin plots with benchmark results

The figure above compares CPython 3.14.6 performance across several distributions using pyperformance. Each violin represents the distribution of per-benchmark mean runtime ratios between an alternative CPython distribution and python-build-standalone on the same platform and architecture. Ratios greater than 1 indicate that python-build-standalone was faster; ratios less than 1 indicate that the alternative distribution was faster.

The horizontal axis uses a logarithmic scale. The vertical marker within each violin indicates the geometric mean of the runtime ratios, which is also expressed as a percentage beside each distribution.

From top to bottom, the distributions shown are:

  • The Docker python:3.14 image for x86-64, providing CPython 3.14.6.
  • A conda-forge Python 3.14.6 environment for linux-64.
  • The system Python 3.14.6 in a fedora:44 x86-64 Docker container.
  • The system Python 3.14.6 in a debian:forky x86-64 Docker container.
  • CPython 3.14.6 from the Python.org macOS installer on an arm64 Mac.
  • A conda-forge Python 3.14.6 environment for osx-arm64.
  • CPython 3.14.6 installed through Homebrew on an arm64 Mac.
  • CPython 3.14.6 from the Python.org Windows installer for x86-64.
  • A conda-forge Python 3.14.6 environment for win-64.

The reference interpreter for each comparison was the corresponding platform- and architecture-matched python-build-standalone CPython 3.14.6 distribution from the 20260623 release, installed using uv.

Benchmarks were run in early to mid-July 2026 and reflect the distributions and packages available during that period.

Supporting material, including Dockerfiles, commands for creating conda environments, and scripts for running the benchmarks, can be found in jjhelmus/cpython-benchmarks. This repository also contains the raw data and the script used to produce the figure.

Benchmark methodology

Benchmarks were executed from a virtual environment created with the reference interpreter into which pyperformance 1.14.0 was installed. Results were collected using:

pyperformance run --rigorous --warmups 2 --output <logfile>

The complete benchmark suite was run at least twice to assess consistency.

Linux benchmarks were run inside Docker containers on an Ubuntu 24.04 host with an Intel Core i9-9900K processor. Hyper-Threading and Intel SpeedStep were disabled.

macOS benchmarks were run on a MacBook Pro with an Apple M5 Max processor.

Windows benchmarks were run on a Windows 11 host with an Intel Core i5-9500 processor. Intel Turbo Boost was disabled; this processor does not support Hyper-Threading.