PyGrunn 2023: python under the hood, why slow and how to speed it up - Mike Huls¶

How python is designed¶

Python is dynamically typed instead of statically typed. This makes it easier for humans to understand and quicker. It makes sure you don’t have to deal with many low-level details. But it can cause errors.

Python is interpreted instead of compiled. If you compile code, it turns your program into machine code. The compiler has nothing further to do with it. With an interpreter, the interpreter is actually running your code when you run it. So the interpreter can handle all the OS-specific optimizations. “Live”. There’s byte code caching, so it’s not that it does double work.

In a compiled language, variables are stored in a fixed location in memory. If you give a variable a new value, that new value is stored in the same memory location. In python, everything is stored as a PyObject with

• a type

• the value

• a reference count

If you assign a new value to a variable name, a new PyObject is created. This can have a different “type”. The old PyObject isn’t referenced anymore, so the reference count is set to zero. It can then be garbage collected.

Python has to do more work, so it is less efficient than a compiled language. Creating PyObjects and names. It also takes quite some more memory due to the PyObject “wrapper”.

The notorious GIL is the Global Interpreter Lock. It ensures thread safe memory access: only one thread executes byte code at the same time. This is especially needed to keep the reference count accurate.

GIL has an exception for I/O: that can happen concurrently. It is the CPU that governed by the GIL.

How can we mitigate bottlenecks, how to speed things up¶

Important to keep in mind: python needs to work a bit harder to enable us to do less work: performance versus productivity. Some generic comments:

• If you have a lot of I/O: threads. A lot of CPU: multiprocessing.

• Watch out with loops where lots of PyObjects need to be created.

• Use built-ins as much as possible. List comprehensions instead of a loop, for instance.

• The same with numpy and pandas built-ins. Applying a lambda to all elements is waaaaaay slower than applying the function to the whole numpy array at the same time where it happens effectively inside numpy.

• If a specific small piece of code is slow, you can try extending python. “pyx”, compiled python code. cy_types is compiled python with extra type hints. You can also go to c or rust code.

• Numba, dask, ray, pypy are alternatives you can look at.