..  -*- rst -*-

================
SciPy benchmarks
================

Benchmarking SciPy with Airspeed Velocity.


Usage
-----

Airspeed Velocity manages building and Python environments by itself,
unless told otherwise. Some of the benchmarking features in
``dev.py`` also tell ASV to use the SciPy compiled by
``dev.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 dev.py bench --submodule sparse.Arithmetic

Compare change in benchmark results with another branch::

    python dev.py bench --compare main --submodule sparse.Arithmetic

Run ASV commands directly (note, this will not set env vars for ``ccache``
and disabling BLAS/LAPACK multi-threading, as ``dev.py`` does)::

    cd benchmarks
    asv run --skip-existing-commits --steps 10 ALL
    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``.

.. _ASV documentation: https://asv.readthedocs.io/


Writing benchmarks
------------------

See `ASV documentation`_ for the 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::

      from .common import safe_import

      with safe_import():
          from scipy.sparse.linalg import onenormest

  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.

- Benchmark versioning: by default ``asv`` invalidates old results
  when there is any code change in the benchmark routine or in
  setup/setup_cache.

  This can be controlled manually by setting a fixed benchmark version
  number, using the ``version`` attribute. See `ASV documentation`_
  for details.

  If set manually, the value needs to be changed manually when old
  results should be invalidated. In case you want to preserve previous
  benchmark results when the benchmark did not previously have a
  manual ``version`` attribute, the automatically computed default
  values can be found in ``results/benchmark.json``.

- Benchmark attributes such as ``params`` and ``param_names`` must be
  the same regardless of whether some features are available, or
  e.g. SCIPY_XSLOW=1 is set.

  Instead, benchmarks that should not be run can be skipped by raising
  ``NotImplementedError`` in ``setup()``.
