For example, there is a decorator `@purefunction` which hints to the translator that a function will always return the same output given the same input, even if it is operating on a data structure that could be considered "opaque".
"One thing the trace optimiser knows is that because program_counter is passed to jit_merge_point as a way of identifying the current position in the user's end program, any calculations based on it must be constant. These are thus easily optimised away, leaving the trace looking as in Figure 3."
So, you're probably right that a fair amount of optimization can come from doing a good job of defining what identifies an execution frame. I'm curious though, if these are the sorts of "low hanging fruit" the author was referring to, or if they're included in the straightforward port of the original C interpreter.
From the section on "Optimizing an RPython JIT" it seems that the he's doing a lot more than just defining his execution frame.
> The first tactic is to remove as many instances of arbitrarily resizable lists as possible. The JIT can never be sure when appending an item to such a list might require a resize, and is thus forced to add (opaque) calls to internal list operations to deal with this possibility.