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 nestedfor-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: -
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 theREQUIRES_RTTI=1environment variable. Under OSX you
can install llvm with HomeBrew:brew install llvm --rtti
-
Install the dependencies from the
requirements.txtfile: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