Improve benchmarks scaling for sub-benchmarks#7431
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scoder wants to merge 1 commit intocython:masterfrom
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Improve benchmarks scaling for sub-benchmarks#7431scoder wants to merge 1 commit intocython:masterfrom
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…st benchmark instead of the slowest. Scale back the timings of sub-benchmarks to the outer scale count to report comparable timings.
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Merged as part of #7454 |
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Jan 22, 2026
Python semantics dictate that we first try the mapping protocol and then the sequence protocol for subscripting. When the index is a C integer, we can optimise perfectly for list/tuple, but all other sequences suffer from having to build a Python `int` object for the index to pass it through the mapping lookup if they implement that (e.g. to support extended slicing, like NumPy arrays). Python 3.10 added type markers (for pattern matching) for explicitly declaring a type as sequence or mapping, called `Py_TPFLAGS_SEQUENCE` and `Py_TPFLAGS_MAPPING`, which can now be checked for quite quickly. If a type is marked as sequence but still implements mapping lookups for slicing, and it supports sequence subscripting, we can avoid the Python `int` creation of the mapping protocol and go straight through the sequence index lookup. With this change, indexing into Python's `array.array` and `memoryview` types is ~60% faster in a micro-benchmark. Using a C integer as dict key got slightly slower but is resolved by adding a separate up-front special case. Future NumPy versions are expected to set the sequence flag and should therefore benefit from this change as well. See numpy/numpy#30519 Benchmark is based on #7431 See https://docs.python.org/3/c-api/typeobj.html#c.Py_TPFLAGS_SEQUENCE #1807 pandas-dev/pandas#55915 pandas-dev/pandas#55179 (comment)
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… by scaling to the fastest benchmark instead of the slowest.
Scale back the timings of sub-benchmarks to the outer scale count to report comparable timings (which will be divided by the outer scale for reporting the per-loop runtime).