GeezerVison

The stats are weird with this PR, lot of misses: https://github.com/faster-cpython/benchmarking-public/blob/main/results/bm-20250116-3.14.0a4%2B-c57fe46/bm-20250116-azure-x86_64-iritkatriel-binaryops-3.14.0a4%2B-c57fe46-pystats-vs-base.md

I think non-negative should be dropped in the news entry now?

And maybe in the title of this pull request, too?

It looks like we are seeing a high proportion of specialization failures for non-compact ints with the & operator. I don't know why that would be. My guess is that some of the benchmarks are using ints as bit vectors and using more than one digit.

Just a wild guess: could those be from enums, which derive from int? Especially flag enums?

Just a wild guess: could those be from enums, which derive from int? Especially flag enums?

I don't think so. We check for int with PyLong_CheckExact.

I suspect the misses might be bm_pyflate:

https://github.com/python/pyperformance/blob/1d9261a7da8fcaa642a36181db8e7c4a306a1303/pyperformance/data-files/benchmarks/bm_pyflate/run_benchmark.py#L153-L163

Other python constructions that would causes misses (but are not in pyperformance afaics) are uuid.uuid4 (and variants) and bitwise logical operations on the hash of python objects.

I suspect the misses might be bm_pyflate:

https://github.com/python/pyperformance/blob/1d9261a7da8fcaa642a36181db8e7c4a306a1303/pyperformance/data-files/benchmarks/bm_pyflate/run_benchmark.py#L153-L163

Could be.

https://github.com/faster-cpython/benchmarking/blob/main/results/bm-20250121-3.14.0a4%2B-6476205/bm-20250121-azure-x86_64-iritkatriel-binaryops-3.14.0a4%2B-6476205-pystats-pyflate.md

(Would be nice if the report was rendered so that it's easy to see which benchmark contributed to a stat.)

Do we have benchmarking numbers for this?

Do we have benchmarking numbers for this?

https://github.com/faster-cpython/benchmarking-public/tree/main/results/bm-20250121-3.14.0a4%2B-6476205

(mdboom edited URL to the public one)

Performance looks neutral within the noise.

Were we expecting a speedup on any particular benchmark, or is there a micro-benchmark that shows a speedup?

Performance looks neutral within the noise.

Were we expecting a speedup on any particular benchmark, or is there a micro-benchmark that shows a speedup?

Here are some microbenchmark numbers:

Old:

>>> for i in range(5):
...     timeit("for i in range(10000):\n\tb = a&i", number=10000, setup="a = 1")
...     
2.517115215305239
2.476386756170541
2.484560175333172
2.4904085099697113
2.4953759447671473

New:

>>> for i in range(5):
...     timeit("for i in range(10000):\n\tb = a&i", number=10000, setup="a = 1")
...     
2.2215038347058
2.1580611928366125
2.1611814140342176
2.154480631928891
2.1730910362675786