NumPy benchmarks

Benchmarking NumPy with Airspeed Velocity.


Airspeed Velocity manages building and Python virtualenvs by itself, unless told otherwise. Some of the benchmarking features in also tell ASV to use the NumPy compiled by To run the benchmarks, you do not need to install a development version of NumPy to your current Python environment.

Before beginning, ensure that airspeed velocity is installed. By default, asv ships with support for anaconda and virtualenv:

pip install asv
pip install virtualenv

After contributing new benchmarks, you should test them locally before submitting a pull request.

To run all benchmarks, navigate to the root NumPy directory at the command line and execute:

python --bench

where --bench activates the benchmark suite instead of the test suite. This builds NumPy and runs all available benchmarks defined in benchmarks/. (Note: this could take a while. Each benchmark is run multiple times to measure the distribution in execution times.)

To run benchmarks from a particular benchmark module, such as, simply append the filename without the extension:

python --bench bench_core

To run a benchmark defined in a class, such as Mandelbrot from

python --bench bench_avx.Mandelbrot

Compare change in benchmark results to another version/commit/branch:

python --bench-compare v1.6.2 bench_core
python --bench-compare 8bf4e9b bench_core
python --bench-compare master bench_core

All of the commands above display the results in plain text in the console, and the results are not saved for comparison with future commits. For greater control, a graphical view, and to have results saved for future comparison you can run ASV commands (record results and generate HTML):

cd benchmarks
asv run -n -e --python=same
asv publish
asv preview

More on how to use asv can be found in ASV documentation Command-line help is available as usual via asv --help and asv run --help.

Writing benchmarks

See ASV documentation for basics on how to write benchmarks.

Some things to consider:

  • The benchmark suite should be importable with any NumPy version.

  • The benchmark parameters etc. should not depend on which NumPy version is installed.

  • Try to keep the runtime of the benchmark reasonable.

  • Prefer ASV’s time_ methods for benchmarking times rather than cooking up time measurements via time.clock, even if it requires some juggling when writing the benchmark.

  • Preparing arrays etc. should generally be put in the setup method rather than the time_ methods, to avoid counting preparation time together with the time of the benchmarked operation.

  • Be mindful that large arrays created with np.empty or np.zeros might not be allocated in physical memory until the memory is accessed. If this is desired behaviour, make sure to comment it in your setup function. If you are benchmarking an algorithm, it is unlikely that a user will be executing said algorithm on a newly created empty/zero array. One can force pagefaults to occur in the setup phase either by calling np.ones or arr.fill(value) after creating the array,