Call julia from Python: which package?

[Chris Angelico]

On Sat, Dec 18, 2021 at 10:01 AM Oscar Benjamin
<oscar.j.benjamin at gmail.com> wrote:
>
> On Fri, 17 Dec 2021 at 22:40, Chris Angelico <rosuav at gmail.com> wrote:
> >
> > On Sat, Dec 18, 2021 at 9:24 AM Oscar Benjamin
> > <oscar.j.benjamin at gmail.com> wrote:
> > > When I timed the result in Julia and in Python I found that the Julia
> > > code was slower than the Python code. Of course I don't know how to
> > > optimise Julia code so I asked one of my colleagues who does (and who
> > > likes to proselytise about Julia). He pointed me to here where the
> > > creator of Julia says "BigInts are currently pretty slow in Julia":
> > > https://stackoverflow.com/questions/37193586/bigints-seem-slow-in-julia#:~:text=BigInts%20are%20currently%20pretty%20slow,that%20basic%20operations%20are%20fast.
> > > I should make clear here that I used the gmpy2 library in Python for
> > > the basic integer operations which is a wrapper around the same gmp
> > > library that is used by Julia. That means that the basic integer
> > > operations were being done by the same gmp library (a C library) in
> > > each case. The timing differences between Python and Julia are purely
> > > about overhead around usage of the same underlying C library.
> >
> > Point of note: "Python actually uses a hybrid approach where small
> > integer values are represented inline and only when values get too
> > large are they represented as BigInts" might be sorta-kinda true for
> > Python 2, but it's not true for Python 3. In all versions of CPython
> > to date, all integers are objects, they're not "represented inline"
> > (there *are* some languages that do this intrinsically, and I think
> > that PyPy can sometimes unbox integers, but CPython never will); and
> > the performance advantage of Py2's machine-sized integers clearly
> > wasn't worth recreating in Py3. (It would be possible to implement it
> > in Py3 as an optimization, while still having the same 'int' type for
> > both, but nobody's done it.)
> >
> > So if Python's integers are faster than Julia's, it's not because
> > there's any sort of hybrid approach, it's simply because CPython is
> > more heavily optimized for that sort of work.
> >
> > (That said: I have no idea whether a StackOverflow answer from 2016 is
> > still applicable.)
>
> To be clear: I wasn't using Python's int type. I used the gmpy2.mpz
> type which is precisely the same mpz from the same gmp library that
> Julia uses. I'm told by my colleague that Julia has a lot of overhead
> when using "heap types" which is possibly the cause of the problem.

Ah, interesting. What's the advantage of using mpz instead of Python's
builtin int?

ChrisA