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Fri 09 May 2014

Why Python is Slow: Looking Under the

Hood

We've all heard it before: Python is slow.

When I teach courses on Python for scientific computing, I make this point very early

(http://nbviewer.ipython.org/github/jakevdp/2013_fall_ASTR599/blob/master/notebooks/11_EicientNumpy.ipynb)

in the course, and tell the students why: it boils down to Python being a dynamically typed,

interpreted language, where values are stored not in dense buers but in scattered objects. And then

I talk about how to get around this by using NumPy, SciPy, and related tools for vectorization of

operations and calling into compiled code, and go on from there.

But I realized something recently: despite the relative accuracy of the above statements, the words

"dynamically-typed-interpreted-buers-vectorization-compiled" probably mean very little to

somebody attending an intro programming seminar. The jargon does little to enlighten people

about what's actually going on "under the hood", so to speak.

So I decided I would write this post, and dive into the details that I usually gloss over. Along the way,

we'll take a look at using Python's standard library to introspect the goings-on of CPython itself. So

whether you're a novice or experienced programmer, I hope you'll learn something from the

following exploration.

# Why Python is Slow

Python is slower than Fortran and C for a variety of reasons:

## 1. Python is Dynamically Typed rather than Statically

Typed.

What this means is that at the time the program executes, the interpreter doesn't know the type of

the variables that are defined. The dierence between a C variable (I'm using C as a stand-in for

compiled languages) and a Python variable is summarized by this diagram:

For a variable in C, the compiler knows the type by its very definition. For a variable in Python, all

you know at the time the program executes is that it's some sort of Python object.

It's easy to see that if you're doing some operation which steps through data in sequence, the numpy

layout will be much more eicient than the Python layout, both in the cost of storage and the cost of

access.

## So Why Use Python?

Given this inherent ineiciency, why would we even think about using Python? Well, it comes down

to this: Dynamic typing makes Python easier to use than C. It's extremely flexible and forgiving,

this flexibility leads to eicient use of development time, and on those occasions that you really

need the optimization of C or Fortran, Python oers easy hooks into compiled libraries. It's why

Python use within many scientific communities has been continually growing. With all that put

together, Python ends up being an extremely eicient language for the overall task of doing science

with code.

# Python meta-hacking: Don't take my word for

Above I've talked about some of the internal structures that make Python tick, but I don't want to

stop there. As I was putting together the above summary, I started hacking around on the internals

of the Python language, and found that the process itself is pretty enlightening.

In the following sections, I'm going to

prove

to you that the above information is correct, by doing

some hacking to expose Python objects using Python itself. Please note that everything below is

written using Python 3.4. Earlier versions of Python have a slightly dierent internal object

structure, and later versions may tweak this further. Please make sure to use the correct version!

Also, most of the code below assumes a 64-bit CPU. If you're on a 32-bit platform, some of the C

types below will have to be adjusted to account for this dierence.

In[1]:

importsys

print("Pythonversion=",sys.version[:5])

## Digging into Python Integers

Integers in Python are easy to create and use:

In[2]:

x=42

print(x)

But the simplicity of this interface belies the complexity of what is happening under the hood. We

briefly discussed the memory layout of Python integers above. Here we'll use Python's built-in

ctypes module to introspect Python's integer type from the Python interpreter itself. But first we

Pythonversion=3.4.0

42

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