Interfacing Python with C/C++ for Performance (2024)

My experience with enhancing Python’s performance by interfacing it with the raw power of C/C++ code.

Author

Paul Norvig

Published

January 10, 2024

Introduction

I’ve been programming for a while and I’ve noticed that sometimes you hit a performance ceiling with Python. That’s where interfacing with C/C++ can really make a difference. It’s a technique I’ve used to speed up my code without giving up the convenience of Python. In this article, I’ll share the knowledge and experience I’ve gained by walking you through methods, best practices, and glimpses of what the future might hold for Python and C/C++ integration.

Introduction to Interfacing Python with C/C++

Interfacing Python with C/C++ can seem daunting at first—I’ve been there. But once you get your hands dirty, the process is refreshingly logical. The motivation is simple: Python is great for ease of use, while C/C++ excels in performance. When you’ve hit a computational wall with Python, dipping into C/C++ for that critical speed-up can be a game-changer.

Let’s start with a straightforward example using Python’s ctypes library which provides C compatible data types, and allows calling functions in DLLs or shared libraries. Here’s a C function that we want to call from Python:

// file: example.c
#include <stdio.h>

int add(int a, int b) {
return a + b;
}

To compile this code to a shared library, you’d execute:

gcc -shared -o example.so -fPIC example.c

In Python, you load this shared library and call the add function like so:

from ctypes import cdll

# Load the shared library
lib = cdll.LoadLibrary('./example.so')

# Call the add function
result = lib.add(3, 4)
print(f"The result is {result}")

Integration can also be achieved using the cffi library which is sometimes preferred for its ease of use with inline declarations and broader ABI support. Here’s how you could rewrite the above example using cffi:

from cffi import FFI

ffi = FFI()

# Define the C function signature
ffi.cdef('int add(int, int);')

# Load the shared library
C = ffi.dlopen('./example.so')

# Call the add function
result = C.add(3, 4)
print(f"The result is {result}")

On the other hand, if you need more integration depth, you can extend Python with modules written in C/C++. Python’s API allows for this through the creation of extension modules. This is where things get more complex, but also where you can tailor your solution with the finest granularity. The extension module approach looks something like this in C:

#include <Python.h>

static PyObject *py_add(PyObject *self, PyObject *args) {
int a, b;

if (!PyArg_ParseTuple(args, "ii", &a, &b)) {
return NULL;
}

int result = a + b;
return PyLong_FromLong(result);
}

static PyMethodDef ExampleMethods[] = {
{"add", py_add, METH_VARARGS, "Add two numbers"},
{NULL, NULL, 0, NULL}
};

static struct PyModuleDef examplemodule = {
PyModuleDef_HEAD_INIT, "example", NULL, -1, ExampleMethods
};

PyMODINIT_FUNC PyInit_example(void) {
return PyModule_Create(&examplemodule);
}

And compile it with something similar to:

python3-config --cflags --ldflags
gcc -shared -o example.so -fPIC example.c $(python3-config --cflags --ldflags)

To use the extension, import it like any other Python module:

import example

result = example.add(3, 4)
print(f"The result is {result}")

Remember, patience is key. I guarantee you’ll run into some hiccups, from segfaults to mysterious Python crashes. Debugging these inter-language issues is part of the learning curve. The Python documentation on extending and embedding is a must-read (Extending Python with C or C++), as is checking out active projects on GitHub that interface Python with C/C++.

There’s exhilaration in getting this right—seeing your Python code execute at the speed of C is deeply satisfying. It’s something of a rite of passage in the optimization world, and I encourage you to embrace the journey with curiosity and tenacity.

Methods for Integrating Python with C/C++

Integrating Python with C/C++ can supercharge your code by combining the simplicity of Python with the sheer speed of C/C++ execution. I’ve found that when performance is paramount, you can sometimes hit a wall with Python. That’s where C/C++ comes into play. Let’s walk through some methods to make them play nice together.

First off, if I’m looking for a quick and simple way to call C code from Python, ctypes is my go-to. The ctypes library is a foreign function interface (FFI) library for Python that provides C compatible data types and allows calling functions in DLLs or shared libraries. It’s built-in, so no additional installation is necessary.

Here’s a quick example where I used ctypes to call a C function from Python:

from ctypes import CDLL, c_double

# load the shared library
mylib = CDLL('path_to_my_lib.so')

# the C function is called "c_sqrt", which calculates the square root of a number
mylib.c_sqrt.argtypes = [c_double]
mylib.c_sqrt.restype = c_double

# now call the C function with a Python float
result = mylib.c_sqrt(9.0)
print(result)  # Should print 3.0 since that's the square root of 9

Then there’s Cython. It’s a static compiler for Python and an extension language, allowing you to use both Python and C-like syntax. With Cython, you can easily speed up Python code by converting it to C, and then compiling it. It’s fantastic for writing glue code that interfaces with C/C++.

Here’s how I’ve done it with Cython before:

# mymodule.pyx
def py_fib(int n):
if n <= 1:
return n
else:
return py_fib(n-1) + py_fib(n-2)

To build this, I’d create a setup.py file:

# setup.py
from setuptools import setup
from Cython.Build import cythonize

setup(
ext_modules=cythonize("mymodule.pyx")
)

And run python setup.py build_ext --inplace to compile.

But when I’m in for more of a learning experience or need full control, I go with a Python extension module with the Python-C API. This involves more boilerplate and you have to be careful with reference counting, but it gives you the most control and best performance.

#include <Python.h>

// a simple C function
static PyObject* c_fib(PyObject* self, PyObject* args) {
int n;
if (!PyArg_ParseTuple(args, "i", &n))
return NULL;

// Fibonacci implementation
int a = 0, b = 1, temp;
while (n-- > 1) {
temp = a + b;
a = b;
b = temp;
}
return PyLong_FromLong(b);
}

// This array tells Python what methods this module has.
static PyMethodDef mymod_methods[] = {
{"c_fib", c_fib, METH_VARARGS, "Calculate the Fibonacci number."},
{NULL, NULL, 0, NULL}   /* sentinel */
};

// This initializes the module.
PyMODINIT_FUNC PyInit_mymod(void) {
return PyModule_Create(&mymodmodule);
}

// The module definition.
static struct PyModuleDef mymodmodule = {
PyModuleDef_HEAD_INIT,
"mymod",   /* name of module */
NULL,      /* module documentation, may be NULL */


-1,        /* size of per-interpreter state of the module,
or -1 if the module keeps state in global variables. */
mymod_methods
};

Compile it with a setup.py script similar to Cython’s and you’re good to go.

Remember, whenever integrating C/C++ with Python, always pay attention to memory management, as it can lead to nasty bugs and memory leaks if ignored.

Each of these methods has its place. ctypes for quick and dirty wrapping, Cython for performance with less hassle, and Python-C API for the scenarios requiring the utmost optimization and control. Choose the one that fits your need and happy coding!

Case Studies of Performance Gains

Interfacing Python with C/C++ can yield considerable performance improvements, especially in computations where Python’s high-level nature becomes a bottleneck. One such example that I worked on involved optimizing a data processing pipeline that dealt with large images. The initial implementation was purely in Python, using libraries like NumPy and PIL for manipulation. The profiling showed that the most significant time was spent on a loop applying filters to each image.

Here’s the original Python snippet:

from PIL import Image

def apply_filter(image):
# Imaginary time-consuming filtering logic
for i in range(image.width):
for j in range(image.height):
# Get the RGB of the pixel
r, g, b = image.getpixel((i, j))
# Apply some filter logic
r, g, b = r*1.5, g*1.5, b*1.5
# Set the pixel to new value
image.putpixel((i, j), (int(r), int(g), int(b)))
return image

# Loading the image
img = Image.open("large_image.jpg")
filtered_img = apply_filter(img)
filtered_img.save("filtered_image.jpg")

The loop was the perfect candidate for optimization. I wrote a C extension for Python that would handle the heavy lifting:

#include <Python.h>

static PyObject* filter_image(PyObject* self, PyObject* args) {
PyObject *listObj, *num;
int r, g, b;
if (!PyArg_ParseTuple(args, "O", &listObj))
return NULL;
// Assume listObj is a list of RGB tuples
for(int i = 0; i < PyList_Size(listObj); i++) {
PyObject* tuple = PyList_GetItem(listObj, i);
r = PyLong_AsLong(PyTuple_GetItem(tuple, 0));
g = PyLong_AsLong(PyTuple_GetItem(tuple, 1));
b = PyLong_AsLong(PyTuple_GetItem(tuple, 2));
r = (int)(r * 1.5);
g = (int)(g * 1.5);
b = (int)(b * 1.5);
PyTuple_SetItem(tuple, 0, PyLong_FromLong(r));
PyTuple_SetItem(tuple, 1, PyLong_FromLong(g));
PyTuple_SetItem(tuple, 2, PyLong_FromLong(b));
}
Py_INCREF(listObj);
return listObj;
}

static PyMethodDef FilterMethods[] = {
{"filter_image", filter_image, METH_VARARGS, "Apply filter to image"},
{NULL, NULL, 0, NULL}
};

static struct PyModuleDef filtermodule = {
PyModuleDef_HEAD_INIT,
"filter",
NULL,


-1,
FilterMethods
};

PyMODINIT_FUNC PyInit_filter(void) {
return PyModule_Create(&filtermodule);
}

Compiled as a shared library, I could then call this function directly from Python:

import filter

# Assuming 'pixels' is a list of RGB tuples
filtered_pixels = filter.filter_image(pixels)

# Saves filtered image code not shown for brevity

The results were impressive. The C-optimized loop completed in seconds compared to several minutes for the original Python loop. That’s a performance gain that’s hard to ignore and a clear demonstration of the potential when merging Python’s ease of use with C’s speed.

Another case study was with a natural language processing task which involves the parsing of large text corpora. The initial pure-Python solution leveraged the ‘re’ module for regular expressions but hit a performance wall with growing datasets.

I created a C++ library that used optimized string manipulation techniques and exposed it to Python using the ctypes module. Here’s an abbreviated version of how it worked:

extern "C" {
void parse_text(const char* text, char* buffer) {
// text parsing and manipulation
strcpy(buffer, parsed_text.c_str());
}
}

In Python:

from ctypes import cdll, c_char_p

# load the shared library
lib = cdll.LoadLibrary("libtextparser.so")

# prepare to call the function
parse_text = lib.parse_text
parse_text.argtypes = [c_char_p, c_char_p]
parse_text.restype = None

input_text = "The quick brown fox jumps over the lazy dog."
buffer = create_string_buffer(512)  # Adjust size as needed

# invoke the C++ function
parse_text(input_text.encode('utf-8'), buffer)

print(buffer.value.decode('utf-8'))

The C++ extension provided faster execution times, lower memory usage, and the ability to scale to larger text corpora without degradation in processing performance.

By interfacing Python with C/C++, I’ve consistently unlocked performance that would have been difficult or impossible to achieve otherwise. It’s a game-changer in scenarios where processing efficiency is critical.

Best Practices and Pitfalls

When combining Python with C or C++ to boost performance, it’s like tuning a high-performance engine; you have to know exactly where to tweak to avoid costly blunders. I’ve been down this path, written the integration code, and hit roadblocks that have taught me valuable lessons.

Firstly, before rushing into native extension modules, ensure you’ve exhausted Python’s optimization strategies. Cython, for instance, lets you sprinkle static type declarations on your Python code, which can already give you a significant speed bump.

cpdef double compute_sum(double[:] arr):
cdef double sum = 0
cdef int i
for i in range(arr.shape[0]):
sum += arr[i]
return sum

Once you’re certain C/C++ is your next step, keep code maintainability in your sights. Remember the Zen of Python: “Readability counts.” Wrapping your C code with Cython can be a winning strategy, allowing you to write Python-like syntax while generating C-level speed.

cdef extern from "mathlib.h":
double compute_sqrt(double x)

def get_sqrt(double number):
return compute_sqrt(number)

Avoid the pitfall of overcomplicating your builds by trying to link Python with a tangled web of C++ classes. Simplicity is your ally. Use extern "C" in C++ to prevent name mangling and keep your interfaces straight to the point.

extern "C" {
double compute_sqrt(double x) {
return sqrt(x);
}
}

Another best practice involves minimizing the overhead of passing data between Python and C/C++. If working with NumPy arrays, for instance, use the NumPy C API or Cython’s typed memoryviews to avoid costly data copies.

from cpython cimport array
import array

cdef extern from "Clib.h":
void c_process_data(double *data, int length)

def process_data_py(double[:] py_data):
cdef array.array data_array = py_data
c_process_data(<double *>data_array.data.as_voidptr, data_array.len)

But, errors will happen, so when they do, ensure they’re signaled properly. Using Python’s exception handling can make your code not only safer but also easier to debug.

cdef extern from "errorlib.h":
int perform_computation() except -1

def compute():
if perform_computation() == -1:
raise ValueError("Computation failed in C code.")

When documentation falls short, there’s no shame in peeking at open-source code from established libraries. Projects like NumPy and Pandas have their source code available on GitHub. Their setups are often intricate, but the way they interface C and Python code can be enlightening.

git clone https://github.com/numpy/numpy.git
cd numpy
grep -r 'PyArrayObject' .

Remember, while you’re targeting performance, you should never trade off the security and sanity of your software. Regularly vet your C/C++ code for buffer overflows and memory leaks. Tools like valgrind can be lifesavers.

valgrind --leak-check=yes python your_script.py

In the end, interfacing Python with C/C++ is a powerful strategy to amp up your application’s performance. Just remember to maintain the elegance of Python while respecting the power and potential pitfalls of C/C++. With a vigilant eye and a focus on best practices, you’ll find yourself writing code that’s not only fast but also robust and maintainable.

Future of Python-C/C++ Interfacing

I’ve spent years dealing now with the delicate dance of Python and C/C++ interoperation. It’s potent stuff. You’ve got the high-level ease of Python and the hardcore performance of C/C++. Blend them right, and you get the best of both worlds. But what does this interplay look like moving forward?

Given current trends, I suspect we’ll see more built-in support for interfacing these languages – making the process far more intuitive. Think tighter integration, allowing Python code to call C/C++ functions almost as if they were native Python functions.

Let’s consider some pseudo-code to illustrate this possible future:

// futurecoolapi.cpp
extern "C" {
void say_hello() {
printf("Hello from C++!");
}
}

And then invoking this in Python might look as smooth as:

# futurecoolapi.py
from cool_futuresupport import c_import

c_lib = c_import("futurecoolapi")
c_lib.say_hello()

In this imagined scenario, cool_futuresupport would be a hypothetical module, part of a future Python standard library or an advanced third-party package that greatly simplifies the loading and calling of C/C++ libraries.

We’re getting more powerful tools, too. Take a look at projects like pybind11 or cppyy. These are game changers that are pushing the envelope of seamless integration. They could well become standard equipment.

Let’s say you’re dealing with a heavy number-crunching function in C++. With pybind11, you can wrap and expose it to Python with ease:

#include <pybind11/pybind11.h>

int heavy_computation(int x) {
// Some intense CPU-bound logic goes here
return x * x;
}

PYBIND11_MODULE(my_module, m) {
m.def("heavy_computation", &heavy_computation, "A heavy computation function");
}

Then in Python:

import my_module

result = my_module.heavy_computation(10)
print(result)  # Output: 100

Machine learning ecosystems, such as TensorFlow and PyTorch, are likely pushing towards more transparent and optimized interfacings with lower-level languages. With the growing emphasis on performance in AI applications, the Python-C/C++ marriage is only going to get stronger.

Further down the line, I see auto-vectorization and parallel processing capabilities being baked into these interfacing strategies – your Python code could automatically take advantage of multi-core processors and SIMD instructions without manual tinkering.

We might also witness more compiler-level optimizations that streamline the transition between Python and C/C++. The dream here is for the JIT compilers to get smarter, maybe by identifying hot spots in your Python code and automatically compiling them down to optimized machine code.

I’m excited to watch and participate in this evolution. My coding practice, along with countless others, may shift as these facilities mature, possibly consolidating around best-in-class libraries and standards that rise from the collective feedback and contributions of a globally engaged developer community.

What’s clear is that we’re heading towards more productivity and performance. Where repetitive boilerplate once slowed us down, we may soon find ourselves rapidly iterating, with the heavy-lifting left to language-bridging tools that are increasingly sharp and sophisticated.

It’s an exciting time to be a developer in this space. Let’s keep pushing the limits.