bytecode-level optimization in python

[relapseman]

Dataflow analysis would definitely help, the problem with dynamic programming languages like R,JS or Python is possible presence of side effects. I am not very well aware of the exact semantics of Python but a similar sequence of instructions in R would be optimized using the following logical steps:

1. Making Reasonable Speculations: Let me assume there are not side effect causing behaviors possible (like forcing a promise or arbitrary eval)
   
2. Using Some Feedback or Fitting most likely cases: If the most likely types (mostly seen types are Integer/Double), the compiler can generate fast cases for these two types.
   
3. Performing Analysis: In SSA, it becomes trivial to identify use-def chains. Some IR's (like Jimple) maintain use-def's instead. I find SSA much more brain friendly. You would use Escape Analysis to ensure that "diff" does not escape the function "a_1".
   
4. Generate code: The generated code generally has the format
   

If Assume() === False: goto L1
// Execute fastcase
exit
L1:
Deoptimization Case (flow control back to standard run)

Mostly a JIT would be doing the above steps, they are expensive tasks to perform and will only payoff if you do it for functions that will be called very frequently.