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Comment by DasIch | Hacker News Reader

Cython is a compiler not an interpreter. Numba requires explicit hinting and can only optimize some undefined (?) subset of Python.

PyPy on the other hand is simply a fast Python interpreter. Not sure what you mean regarding the type information gathered by the JIT.

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For example does PyPy always warm up, or can it store "warmed up" version of some function (where type of objects is inferred)? I know that this is not in accordance with highly dynamic nature of Python, but not all functions are highly dynamic.

My understanding (from a previous HN discussion about javascript) is that since the output from a "just-in-time compiler" is actually machine code generated on the fly, it includes direct references to memory locations that are only valid for the lifetime of the process. So the output of a JIT is simply not in a format that can be saved and reloaded later.

Actually its possible to have a JIT like that but it is uncommon.

Do you mean can you save the state of the JIT for future use?

Yeah. Can you? Considering CPython already generates .pyc and checks timestamps, keeping some kind of JIT cache around wouldn't be a stretch.

No, the state of the JIT cannot be saved between executions.

I can't speak for python, but I do know that the impact of translating code for Racket's JIT (provided by GNU lightning, not LLVM) is negligable (ie. takes less time than the time you gain by JITting

Cython is not actually a compiler. It is essentially a more convenient way of writing C/C++ extension modules. It produces C/C++ glue code which is then compiled by an actual compiler like gcc. By tapping into the existing C API of cpython it is simpler than PyPy & NumPy which work on a lower level, but on the other hand Cython does little optimization for you.

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