Not Brandt but regarding the second question
It seems there is a general contradiction between working on tiny unit of works (micro-ops) and using a JIT compilation scheme that doesn’t optimize accross micro-ops. Is it possible to break out of this contradiction while still benefitting from the advantages of the copy-and-patch approach?
Yes there is some perf lost from having smaller units of compilation (micro ops vs normal ops). My own estimations from Jeff Glass’ superinstructions experiment is between 1-4% performance loss on pyperformance.
The JIT compiler doesn’t optimize acrosss micro-ops. That is the job of the trace optimizer (or tier 2 optimizer). The optimizer lives at cpython/Python/optimizer_analysis.c at main · python/cpython · GitHub . Currently it doesn’t do much apart from simple elimination of type checks and some other guards. In 3.13 we just set up the main infrastructure for it to collect type information. There are plans for significantly more optimizations in 3.14.