README.rst

SciPy benchmarks

Benchmarking Scipy with Airspeed Velocity.

Usage

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

Run a benchmark against currently checked out Scipy version (don't record the result):

python runtests.py --bench sparse.Arithmetic

Compare change in benchmark results to another branch:

python runtests.py --bench-compare master sparse.Arithmetic

Run benchmarks against the system-installed SciPy rather than rebuilding:

python runtests.py -n --bench sparse.Arithmetic

Run ASV commands:

cd benchmarks
./run.py run --skip-existing-commits --steps 10 ALL
./run.py publish
./run.py preview

The run.py script sets up some environment variables and does other minor maintenance jobs for you. The benchmark suite is runnable directly using the asv command.

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:

• When importing things from Scipy on the top of the test files, do it as:

  try:
      from scipy.sparse.linalg import onenormest
  except ImportError:
      pass

  The benchmark files need to be importable also when benchmarking old versions of Scipy. The benchmarks themselves don't need any guarding against missing features --- only the top-level imports.

• Try to keep the runtime of the benchmark reasonable.
• Use 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.
• Use run_monitored from common.py if you need to measure memory usage.