Numba Versus C++ by David Butts, Gautham Dharuman, Bill Punch and Michael S. Murillo.
Python is a programming language that first appeared in 1991; soon, it will have its 27th birthday. Python was created not as a fast scientific language, but rather as a general-purpose language. You can use Python as a simple scripting language or as an object-oriented language or as a functional language…and beyond; it is very flexible. Today, it is used across an extremely wide range of disciplines and is used by many companies. As such, it has an enormous number of libraries and conferences that attract thousands of people every year.
But, Python is an interpreted language, so it is very slow. Just how slow? It depends, but you can count on about 10-100 times as slow as, say, C/C++. If you want fast code, the general rule is: don’t use Python. However, a few more moments of thought lead to a more nuanced perspective. What if you spend most of the time coding, and little time actually running the code? Perhaps your familiarity with the (slow) language, or its vast set of libraries, actually saves you time overall? And, what if you learned a few tricks that made your Python code itself a bit faster? Maybe that is enough for your needs? In the end, for true high performance computing applications, you will want to explore fast languages like C++; but, not all of our needs fall into that category.
As another example, consider the fact that many applications use two languages, one for the core code and one for the wrapper code; this allows for a smoother interface between the user and the core code. A common use case is C or C++ wrapped by, of course, Python. As a user, you may not even know that the code you are using is in another language! Such a situation is referred to as the “two-language problem”. This situation is great provided you don’t need to work in the core code, or you don’t mind working in two languages – some people don’t mind, but some do. The question then arises: if you are one of those people who would like to work only in the wrapper language, because it was chosen for its user friendliness, what options are available to make that language (Python in this example) fast enough that it can also be used for the core code?
We wanted to explore these ideas a bit further by writing a code in both Python and C++. Our past experience suggested that while Python is very slow, it could be made about as fast as C using the crazily-simple-to-use library Numba. Our basic comparisons here are: basic Python, Numba and C++. Because we are not religious about Python, and you shouldn’t be either, we invited expert C++ programmers to have the chance to speed up the C++ as much as they could (and, boy could they!).
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This webpage is highly annoying, in both Mozilla and Chrome. You’ll have to visit to get the full impact.
It is, however, also a great post on using Numba to obtain much faster results while still using Python. The use of Wolfram CAs (cellular automata) as examples is an added bonus.
Enjoy!