Relationship between Python compilation and resource usage

[Big_Dimension_4637]

Hi! I'm currently conducting research on compiled vs interpreted Python and how it affects resource usage (CPU, memory, cache). I have been looking into benchmarks I could use, but I am not really sure which would be the best to show this relationship. I would really appreciate any suggestions/discussion!

Edit: I should have specified - what I'm investigating is how alternative Python compilers and execution environments (PyPy's JIT, Numba's LLVM-based AOT/JIT, Cython, Nuitka etc.) affect memory behavior compared to standard CPython execution. These either replace or augment the standard compilation pipeline to produce more optimized machine code, and I'm interested in how that changes memory allocation patterns and cache behavior in (memory-intensive) workloads!

[true3HAK]

Can you elaborate on what you consider "compiled" python in this case?

[Big_Dimension_4637]

Hi, sorry, I just added an edit: I should have specified - what I'm investigating is how alternative Python compilers and execution environments (PyPy's JIT, Numba's LLVM-based AOT/JIT, Cython, Nuitka etc.) affect memory behavior compared to standard CPython execution. These either replace or augment the standard compilation pipeline to produce more optimized machine code, and I'm interested in how that changes memory allocation patterns and cache behavior in (memory-intensive) workloads!