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An efficient method of calling C++ functions from numba using clang++/ctypes/rbc

Published October 19, 2021



Pearu Peterson

The aim of this post is to explore a method of calling C++ library functions from Numba compiled functions --- Python functions that are decorated with numba.jit(nopython=True).

While there exist ways to wrap C++ codes to Python (see Appendix below), calling these wrappers from Numba compiled functions is often not as straightforward and efficient as one would hope.The underlying problem for this inefficiency is that the Python/C++ wrappers are designed to be called with Python objects as inputs. For instance, with keeping in mind the aim of this post, a call to a Numba compiled function would involve converting a Python object to a low-level object, say, to some C/C++ equivalent intrinsic type or structure, then converting it back to Python object to be passed to the Python/C++ wrapper function. Then this wrapper function would convert the Python object (again) to an equivalent C/C++ type object which can be finally passed to the underlying C++ library function. The return value of the C++ function would be transformed several times as well, just in the opposite direction of the function's calling sequence. In totality, all these object transformations may build up a considerable overhead of calling otherwise highly efficient C++ library functions from Python.

Another difficulty with calling C++ library functions from Numba originates from the name-mangling that C++ compilers apply to function names in order to support function/method overloadings as well as other relevant C++ language features. In principle, one would be able to call such a C++ library function directly from any Numba compiled function (using the numba.cfunc feature) if one would knows how the C++ compiler transforms the function name internally . However, the name-mangling algorithm is C++ compiler vendor dependent, and in practice, it would be hard to predict the mangled name in a portable manner. tool

In this post, we'll introduce a straightforward and efficient method of calling C++ library functions from Numba compiled functions that circumvents the name-mangling problem. The method is based on determining the addresses of C++ library functions at runtime which together with functions signatures are then used to set up a highly efficient calling sequence. This method will require creating a small C/C++ wrapper library that contains extern "C"-attributed functions which return the addresses of C++ library functions and can be easily called from Python using various techniques, here we use ctypes.

We provide here a Python script that auto-generates, from a user-supplied C++ header and source files, the C/C++ wrapper library as well as a Python ctypes wrapper module. The Python module contains ctypes definitions of C++ library functions that Numba compiled functions are able to call directly without requiring the expensive and redundant object transformations mentioned above. An alternative to using ctypes in the Python wrapper module would be to use the Numba Wrapper Address Protocol - WAP . Its usage should be considered if the ctypes "C++ expressiveness" turns out to be insufficient.

Currently, the supported features in the tool include:

  • wrapping of C++ library functions with scalar inputs and return values,
  • supporting C++ functions which may be defined inside C++ namespaces,
  • supporting C++ functions which may be static class member functions.

The tool can be extended to support other C++ features such as:

  • creating C++ class/struct instances from Python,
  • passing C++ class/struct instances to-and-fro between languages,
  • calling the methods of C++ class/struct instances,
  • supporting pointer types as function inputs and return values,
  • etc.

The tool uses CLang to parse C++ header files and to build the C/C++ wrapper shared library. It also uses the RBC - Remote Backend Compiler to parse the signatures of C++ functions for convenience.


As a prerequisite, let's create a Conda environment as follows

$ conda create -n cxx2py-demo -c conda-forge numba rbc cxx-compiler clangdev
$ conda activate cxx2py-demo

We assume that the script is copied to the current working directory and is functional:

$ python --help
usage: [-h] [-m MODULENAME] [--clang-exe CLANG_EXE]
[--clang-ast-dump-flags CLANG_AST_DUMP_FLAGS]
[--clang-build-flags CLANG_BUILD_FLAGS]
[--clang-extra-flags CLANG_EXTRA_FLAGS]
[--build] [--verbose]
file [file ...]
Generate ctypes wrappers to C++ library functions
positional arguments:
file C++ header/source file
optional arguments:
-h, --help show this help message and exit
Python module name of ctypes wrappers (default: untitled)
--clang-exe CLANG_EXE
Path to clang compiler (default: clang++)
--clang-ast-dump-flags CLANG_AST_DUMP_FLAGS
Override flags to clang ast dump command (default:
'-Xclang -ast-dump -fsyntax-only -fno-diagnostics-color')
--clang-build-flags CLANG_BUILD_FLAGS
Override flags to clang build shared library command
(default: '-shared -fPIC')
--clang-extra-flags CLANG_EXTRA_FLAGS
Extra flags to clang command (default: '')
--build Build shared library (default: False)
--verbose Be verbose (default: False)

Next, let us consider the following C++ header and source file that we will use as a model of a C++ library:

/* File: foo.hpp */
#include <iostream>
int foo(int a);
namespace ns {
namespace ns2 {
double bar(double x);
class BarCls {
BarCls(double a): a_(a) {}
double get_a() { return a_; }
static int fun() { return 54321; }
double a_;
/* File: foo.cpp */
int foo(int a) {
std::cout << "in foo(" << std::to_string(a) << ")" << std::endl;
return a + 123;
namespace ns { namespace ns2 {
double bar(double a) {
std::cout << "in ns::ns2::bar(" << std::to_string(a) << ")" << std::endl;
return a + 12.3;

We build a Python ctypes wrapper library using

$ python -m libfoo foo.hpp foo.cpp --build
As a quick test, try running:
LD_LIBRARY_PATH=. python -c "import untitled as m; print(m.__all__)"

that will create three files in the current directory:

$ ls *libfoo*

Notice that the generated cxx2py_libfoo.cpp file contains light-weight C functions for returning the addresses of C++ functions:

#include <memory>
#include <cstdint>
#include "foo.hpp"
extern "C" intptr_t get_foo_address() {
/* int (int) */
return reinterpret_cast<intptr_t>(std::addressof(foo));
extern "C" intptr_t get_ns__ns2__bar_address() {
/* double (double) */
return reinterpret_cast<intptr_t>(std::addressof(ns::ns2::bar));
extern "C" intptr_t get_ns__BarCls__fun_address() {
/* int () */
return reinterpret_cast<intptr_t>(std::addressof(ns::BarCls::fun));

The cxx2py_libfoo.cpp file is built into the shared library when --build flag is used.

Let's test the wrapper module libfoo in Python:

$ export LD_LIBRARY_PATH=. # this makes sure that ctypes is able to find the shared library

$ python
>>> import libfoo
>>> libfoo.__all__
['foo', 'ns__ns2__bar', 'ns__BarCls__fun']
in foo(5)
>>> libfoo.ns__ns2__bar(1.2)
in ns::ns2::bar(1.200000)
>>> libfoo.ns__BarCls__fun()

that is, the C++ library functions can be called directly from Python thanks to ctypes!.

Moreover, the C++ library functions can be called from Numba compiled functions. For example:

>>> import numba
>>> @numba.njit
.def fun(x):
. return + 2)
>>> fun(5)
in foo(7)


In this post, we outlined a method of calling C++ library functions from Python with an emphasis on their usage from Numba compiled functions with minimal overhead. While the provided tool currently supports only wrapping C++ functions with scalar inputs and return values, it can be easily extended to support other C++ features as well.


I thank Breno Campos and Guilherme Leobas for discussions and for sharing their expertise on the LLVM Project.

Appendix: A list of Python/C/C++ connectivity tools

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