python-benchmarks

A set of benchmark problems and implementations for Python.

Results

numfocus.github.io/python-benchmarks

Motivation

This repository is the result of a discussion of started by @aterrel at SciPy 2013 where people interested in the development of compiler technologies for the Python programming language shared design decisions.

The goal of this repository to gather Python implementations of realistic use cases where:

• naive code written with the CPython interpreter is too slow to be of practical use,

• an implementation of the algorithm cannot be efficiently vectorized using NumPy primitives (for instance by involving nested for-loops)

Initial use cases focus on data processing tasks such as machine learning and signal processing.

For each benchmark, we would like to gather:

• a naive pure python implementation (optionally using NumPy for large homogeneous numerical datastructures) run using CPython

• variants of the Python version that should be able to run the naive pure Python version with minimal code change:

  • JIT compiler packaged as a library for CPython such as:

    • Numba or
    • Parakeet

  • JIT compiler implemented in an alternative Python interpreter such as:

    • PyPy optionally with NumPyPy

  • Python to C/C++ code translation + compiled extension for the CPython interpreter such as done by:

    • Pythran
    • Shed Skin

• pure Python programs that explicitly represent the computation as a graph of Python objects and use code generation and a compiler to dynamically build a compiled extension such as done by Theano

• alternative language implementations in Cython, C or Fortran with Python bindings to serve as speed reference.

Running

To run all the benchmarks:

python run_benchmarks.py

To run the benchmarks of a specific folder:

python run_benchmarks.py --folders pairwise

To run only the benchmarks with specific platforms:

python run_benchmarks.py --platforms numba parakeet cython

To ignore previously collected data:

python run_benchmarks.py --ignore-data

To see all the tracebacks of the collected errors:

python run_benchmarks.py --log-level debug

To open a browser on the generated HTML report page:

python run_benchmarks.py --open-report

To publish the generated report to github (assuming you want to push to origin):

make github

Or to another remote alias:

WEB_ALIAS_REPO=upstream make github

Dependencies

Using pip

• Some dependencies use llvmpy that require to have llvm built with the REQUIRES_RTTI=1 environment variable. Under OSX you can install llvm with HomeBrew:

  brew install llvm --rtti

• Install the dependencies from the requirements.txt file:

  pip install -r requirements.txt

Note: some packages (pythran and ply) have a depency on SciPy which is complicated and slow to install from source because of the need of a gfortran compiler and a large C++ code base. It is recommended to install a binary package for SciPy (see http://scipy.org/install.html for instructions).

Using conda / Anaconda

TODO

Non CPython dependencies

You can also install PyPy from http://pypy.org