On Fri, 10 Aug 2018 19:15:11 +0200 Armin Rigo email@example.com wrote:
Currently, the C API only allows Psyco-style JITting (much slower than PyPy). All three other points might not be possible at all without a seriously modified C API. Why? I have no proof, but only circumstantial evidence. Each of (2), (3), (4) has been done in at least one other implementation: PyPy, Jython and IronPython. Each of these implementation has also got its share of troubles with emulating the CPython C API. You can continue to think that the C API has got nothing to do with it. I tend to think the opposite. The continued absence of major performance improvements for either CPython itself or for any alternative Python implementation that *does* support the C API natively is probably proof enough---I think that enough time has passed, by now, to make this argument.
Jython and IronPython never got significant manpower AFAIK, so even without being hindered by the C API, chances are they would never have gotten very far. Both do not even seem to have stable releases implementing the Python 3 language...
That leaves us with CPython and PyPy, which are only two data points. And there are enough differences, AFAIK, between those two that picking up "supports the C API natively" as the primary factor leading to a performance difference sounds arbitrary.
(the major difference being IMHO that PyPy is written in RPython, which opens up possibilities that are not realistic with a C implementation, such as the JIT being automatically able to inspect implementations of core / stdlib primitives; in a CPython-based JIT such as Numba, you have to reimplement all those primitives in a form that's friendly to the JIT compiler)