Python and C interaction: Part I - An Introduction
Too much is said of Python as a glue. Although many Python advocates use this to value Python over other languages, the truth is that any high-level scripting language (think of perl, ruby or incipient Julia). However, and despite the Global Interpreter Lock, Python’s Hardest Problem, Python (especially v2.7+) is the main scripting language in scientific applications due mostly to the high availiability of scientific libraries: NumPy, SciPy, SciKitLearn, et al. The advantage over a compiled language becomes evident once we compare a simple code for calculating averages in Python and in C:
Not only the Python syntax is much cleaner, but also keep in mind that the C code has to be compiled previous to execution, say to
add_numbers.e. The question now is, why do we use C then? The answer is obvious: both codes do the exact same thing, but while the Python code takes
8.047 s 1, the C code takes
0.284 s: 28 times faster2. So, how can we overcome these issues? Anyone who has used NumPy, knows that this is problem goes exactly to its locker. Let’s take a look at the Python code with NumPy:
Python (with NumPy)
With this new code we get a time of
0.266 s, comparable with C. However, we also know that numpy is quite strict: we have to use vectors and try to write our implementation so we use only NumPy functions. Hence, we’ve lost Python versatility. The question is then: can we get the versatility of the scripting language and the speed of a compiled language? There are two ways to do this:
We implement a scripting language in C: Much as Python is written in C, we can write our program in C and also code a scripting language surrounding it. The main problem is that you have to reimplement a lot of things in C that are not time critical and might end up with a quite shaky and not straightforward scripting language. A good example of a rewrite of a full fledged scripting language from C code is LAMMPS.
We write Python and C code separately and we link them: If every language deep down has to be machine code, we have to be able to communicate any languages we want. In this case, we can write a) Python code; b) C code; and c) a C/Python API that takes care of the communication. We only write the time consuming part in C and we can use the Python flexibility, scriptability, duck-typability and everything-that-is-good-in-this-worldability. The infamous NumPy library actually does this in order to get its speedup3.
We will focus, of course in this last option. But the C/Python API itself has many sub-choices:
Write the whole module in C, Python-like: If Python was written in C, we can write any Python module in C. This is, for example, the
Python.hAPI. I have done this a couple of times, and can assure it can be rather painful, since you have to rewrite many of the Python parsing in C, and also take into account memory management. The biggest advantage, however, is that you write the C code that is going to be executed, and consequently you can make fine tuning choices that can be critical in performance.
Write the module in Python and generate C code from it: This is the Cython way. As you can see from the examples in the webpage, we write pure Python code, and then convert it to C code This translation tween langauges of similar level languages is call transpilation (portmanteau of translation and compilation). Cython is, therefore, a transpiler4. Although this looks very good on paper, the truth is that, as in any compiler, a lot of work needs to be done in order to get good optimizations5. The fine tuning you could do with the Python C-API now cannot be done.
Write the module in C and interface it with Python: If we could make Python aware of the crude, low-level C data structures (
float), we can call the functions from C shared library with them. This is the idea behind ctypes. You write your C function as you would if you were only writing C code, but then call it from Python with the proper data types. This way, we can write C code as in case 1, but it has the great advantage that the interface and memory management is done in Python.
I would like to discuss briefly option 2 Cython and other close relatives like Numba: although they can give relatively good speedups with very little work from already written Python code, they do not go the extra mile. They are obviously the choice if you know nothing of C. Or, if you are looking to get that creeping Python script to take 10 minutes instead of waiting an hour, maybe give them a chance first. But if you are writing with HPC in mind from the beginning, they will definitely fall short6.
Options 1 and 3 are very similar and, in my opinion, option 3 actually supersedes option 1. This is just an introduction and aims to give a wide view of the possibilities. In part II we will show some examples of ctypes and its basic usage (nothing that cannot be found just searching online). Part III will be dedicated to an advanced and not often discussed use.
Obviously all performance measures are highly hardware dependent. ↩
This means that a one hour simulation takes one day if we use Python. While 28x is obviously a huge boost, I remember discussing once with a (really good) colleague, who did not work on HPC, about 2x-5x speedups. I am still quite amazed by the fact that he was not amazed by a 5x speedup. The point of this being that you have to think whether its worth it to perform a full-fledged optimization. ↩
Bonus points: since you are actually running a shared library, you release the Global Interpreter Lock, and in the running shared library you can thread. ↩
We can argue whether it’s a transpiler or a compiler. But it’s not worth it ↩
The same goes for writing C code. No matter how good the compiler is, you won’t get better performance than good, hand-written assembly code. ↩
F2PY actually does the inverse. From pure FORTRAN code, it automatically generates the Python interface; therefore we get the fine tuning in the compiled language and the eventually non-optimized autogenerated code is on the already slow, not time critical Python side. ↩
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