Le 2018-01-28 à 05:11, Yury V. Zaytsev a écrit :
>
> I think that you might be confused about the fundamentals of the
> technologies involved here.
>
Yes, I admit, i'm really just starting to understand PyPy fundamentals
and LLVM.
> Once you translate a Django app into C code (let's assume this is
> actually possible for the sake of the argument) and then compile it
> into machine code using clang there is nothing more left for a JIT to
> operate upon, because machine code is interpreted directly by the CPU.
>
I'm really sure its possible to generate a C or C++ file from
human-generated Python code. So far, I want to use the LLVM backend
(PyPy) to translate CPython classes into a tracing JIT compiler...
> Tracing JIT engines like PyPy translate bytecode into machine code on
> the fly taking into account invariants discovered during runtime,
> which theoretically enables them to outperform machine code generated
> without knowing the data it processes, and, in any case, run a lot
> faster than the interpreted byte code.
I'm positive you can use PyPy in embedded C/C++ applications to enable
trace compilation of Python objects.
>
> A possible source of confusion is that people often speak of speeding
> things up with LLVM (or nowadays even GCC) JIT; in most cases this
> amounts to so called method level JITs where specific isolated
> functions are compiled on the fly into machine code by the
> corresponding JIT backend and then called from the bytecode
> interpreter instead of actually interpreting the original bytecode for
> the method.
>
JIT is cool because it can theoretically makes Django and Python web
apps outperform C applications.
> Anyways, having that said, I can't even infer what your original line
> of thinking was to embed what into what to speed up what exactly...
>
I'm looking to use JIT as a replacement for Cython in a upcoming
Django-hotsauce release. :)
Cheers,
Etienne
--
Etienne Robillard
tkadm30@yandex.com
https://www.isotopesoftware.ca/
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