Stop timing NumPy in the ASV benchmarks

[antonwolfy]

This PR removes the numpy executor from the ASV benchmark suites, so bench_elementwise.py, bench_linalg.py and bench_random.py now time only dpnp.

The suites track dpnp performance from commit to commit, and nothing compares the dpnp and NumPy timings with each other. A dpnp change cannot move a NumPy timing, so those points carry no signal. They did have two costs:

• They made up half of the timed points in these three suites (470 of 940).
• Machine noise on them was reported as a dpnp regression. A master commit that only bumped pre-commit hooks was flagged solely because of MatMul.time_matmul('numpy', 16, 'float64') at 1.22x. On a recent PR, two of the three reported regressions were NumPy points, including MatMul.time_dot('numpy', 64, 'float32') at 1.6x, with the same NumPy and MKL builds on both sides.

The change keeps the recorded results comparable:

• executor stays as a single-value axis ('dpnp'), so result names such as time_dot('dpnp', 64, 'float32') are unchanged.
• The setup and time_* methods are not modified, including the numpy branches some setup methods no longer use. ASV versions a benchmark by the source of these methods, so editing them would make every result incomparable with the recorded baseline. All 29 such methods are byte-identical to master.

Against a baseline recorded before this change, the NumPy points exist only on the baseline side. asv compare treats them as skipped, so --only-changed hides them and they are not counted as regressions.

benchmarks/README.md is updated to match and gets a new "NumPy is not timed" section explaining the above.

• Have you provided a meaningful PR description?
• Have you added a test, reproducer or referred to an issue with a reproducer?
• Have you tested your changes locally for CPU and GPU devices?
• Have you made sure that new changes do not introduce compiler warnings?
• Have you checked performance impact of proposed changes?
• Have you added documentation for your changes, if necessary?
• Have you added your changes to the changelog?

[github-actions]

View rendered docs @ https://intelpython.github.io/dpnp/index.html

[coveralls]

coverage: 78.669% (+0.002%) from 78.667% — benchmarks-drop-numpy-executor into master

[antonwolfy]

Note, the 65536-size unary entries still keep making about half the runs UNSTABLE, but will be addressed in the follow-up.

[antonwolfy]

Note, the 65536-size unary entries still keep making about half the runs UNSTABLE, but will be addressed in the follow-up.

[github-actions]

Array API standard conformance tests for dpnp=0.21.0dev14=np2py314h8d9cdd5_6 ran successfully.
Passed: 1376
Failed: 0
Skipped: 6

[vlad-perevezentsev]

LGTM
Thank you @antonwolfy !