Ist das Problem da jetzt die Gummierung der Briefmarke oder sind es die scharfen Kanten der Postkarten?
I'm giving an in-depth learn-from-a-core-dev Cython training at the Python Academy in Leipzig (Germany) next month, October 16-17. In two days, the course will cover everything from a Cython intro up to the point where you bring Python code to C speed and use C/C++ libraries and data types in your code.
Read more on it here:
What is Cython?
Cython is an optimising static compiler for Python that makes writing C extensions as easy as Python itself. It greatly extends the limits of the Python language and thus has found a large user base in the Scientific Computing community. It also powers various well-known extension modules in the Python package index. Cython is a great way to extend the CPython runtime with native code when performance matters.
Who am I?
I'm Stefan Behnel, a long-time Cython core developer specialising in High-Performance Python trainings and fast code. See my website.
I keep seeing a lot of code around that does this:
import re _MATCH_DIGITS_RE = re.compile('[0-9]+')
and then uses this regular expression only in one place, e.g. like this:
def func(): numbers = _MATCH_DIGITS_RE.findall(input) ...
Python's re module actually uses expression caching internally, so it's very unlikely that this is any faster in practice than just writing this:
def func(): numbers = re.findall('[0-9]+', input) ...
Which is a shorter and much more straight forward way to write what's going on. Now, for longer and more complex regular expressions, this can actually get out of hand and it does help to give them a readable name. However, all-upper-case constant names tend to be pretty far from readable. So, I always wonder why people don't just write this using a bound method:
_find_numbers = re.compile('[0-9]+').findall def func(): numbers = _find_numbers(input) ...
I find this much clearer to read. And it nicely abstracts the code that uses the function-like callable _find_numbers() from the underlying implementation, which (in case you really want to know) happens to be a method of a compiled regular expression object.
I spent some time during the last two weeks reducing the call overhead for Python functions and methods in Cython. It was already quite low compared to CPython before, about 30-40% faster, but profiling then made me stumble over the fact that method calls in CPython really just do one thing: they repack the argument tuple and prepend the 'self' object to it. However, that is done right after Cython has carefully packed up exactly that argument tuple in the first place, so by simply inlining what PyMethodObject does, we can avoid packing tuples twice.
Avoiding to create a PyMethodObject at all may also appear as an interesting goal, but doing that is totally not easy (it happens during attribute lookup) and it's also most likely not worth it as method objects are created from a freelist, which makes their instantiation very fast. Method objects also hold actual state that the caller must receive: the underlying function and the self object. So getting rid of them will severly complicate things without a major gain to expect.
Another obvious optimisation, however, is that Python code calls into C implemented functions quite often, and if those are implemented as specialised functions that take exactly one or no argument (METH_O/METH_NOARGS), then the tuple packing and unpacking can be avoided completely. Together with the method call optimisation, this means that Cython can now call very simple methods without creating an argument tuple, and less simple ones without redundantly creating a second argument tuple.
I implemented these optimisations and they immediately blew up the method call micro benchmarks in Python's benchmark suite from about 1/3 to 2-3 times faster than CPython 3.5 (pre). Those are only simple micro benchmarks, so any real world code will benefit substantially less overall. However, it turned out that a couple of benchmarks in the suite that are based on real production code ended up loosing 5-15% of their total runtime. That's quite remarkable, given that the code they call actually does something (much) more heavy weight than the call overhead itself. I'm still tuning it a bit, but so far am really happy with this result.