Python High Performance - Second Edition

Object mode versus native mode

So far, we have shown how Numba behaves when handling a fairly simple function. In this case, Numba worked exceptionally well, and we obtained great performance on arrays and lists.

The degree of optimization obtainable from Numba depends on how well Numba is able to infer the variable types and how well it can translate those standard Python operations to fast type-specific versions. If this happens, the interpreter is side-stepped and we can get performance gains similar to those of Cython.

When Numba cannot infer variable types, it will still try and compile the code, reverting to the interpreter when the types can't be determined or when certain operations are unsupported. In Numba, this is called object ...