myself> Ok. Now I'm lost. We have so many patches :-) Which one do you prefer?

I read again fully this *old* issue, well, *almost* all messages.

Well, it's clear that no consensus was found yet :-) I see two main trends: optimize most cases (optimize most operators for int and float,  ex: fastint5_4.patch) versus optimize very few cases to limit changes and to limit effects on ceval.c (ex: inline-2.patch).

Marc-Andre and Antoine asked to not stick to micro-optimizations but think wider: run macro benchmarks, like perf.py, and suggest to use PyPy, Numba, Cython & cie for users who use best performances on numeric functions.

They also warned about subtle side-effects of any kind of change on ceval.c which may be counter-productive. It was shown in the long list of patches that some of them introduced performance *regressions*.

I don't expect that CPython can beat any compiler emiting machine code. CPython will always have to pay the price of boxing/unboxing and its loop evaluating bytecode. We can do *better*, the question is "how far?".

I think that we gone far enough on investigation *all* different options to optimize 1+2 ;-) Each option was micro-benchmarked very carefully.

Now I suggest to focus on *macro* benchmarks to help use to take a decision. I will run perf.py on fastint5_4.patch and inline-2.patch.