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Alternative to Cxx.jl and CxxWrap.jl. Both are great libraries. Cxx.jl lets people write C++ code in Julia, whereas CxxWrap.jl lets people write Julia code in C++.

The design of CxxInterface.jl is simpler than either Cxx.jl or CxxWrap.jl: Wrapper functions are written in Julia, and they generate respective C++ wrapper functions via string manipulation that are called via ccall. String manipulation is somewhat tedious, but its large advantage is that it is a well-supported standard that works independent of C++ compiler and Julia version. The current version of CxxInterface.jl should continue to work for later versions of Julia without undue maintenance overhead.

This package supports not only C++, but also C, Fortran, and Rust as external languages.


Let's assume that there is a C++ library AddIntegers that provides a function

namespace AI {
    int add_int(int x, int y);

that we want to wrap in Julia. This would look as follows:

using CxxInterface
using AddIntegers_jll

cxxprelude("AddIntegers.cxx", """
    #include <add_integers.hxx>

eval(cxxfunction(FnName(:add_int, "add_int", libAddIntegers),
                 FnResult(Cint, "int"),
                 [FnArg(:x, Cint, "x", "int"),
                  FnArg(:y, Cint, "y", "int")],
                 "return AI::add_int(x, y);"))

The prelude defines the name of the file that will hold the generated C++ code, as well as any C++ statements that are necessary at the beginning of that file.

Most arguments to cxxfunction come in pairs, defining what happens on the Julia side (using symbols and Julia types) as well as what happens on the C++ side (using strings). In detail:

  • The wrapper function has the name add_int both in Julia and C++
  • The first function argument is called x in both Julia and C++, and has the type Cint in Julia and int in C++
  • Similarly for the second function argument y
  • The C++ wrapper code is given as a string.

When this module is loaded, it will generate the Julia function

function add_int(x::Cint, y::Cint)
    return ccall(("add_int", libAddIntegers), Cint, (Cint, Cint), x, y)

It will also generate the respective C++ code as a string:

#include <add_integers.hxx>

extern "C" int add_int(int x, int y) {
    return AI::add_int(x, y);

This C++ code can be written to a file and compiled with a C++ compiler. Ideally, this will happen within a BinaryBuilder build script that then also compiles the generated code for multiple architecture into a JLL package. Presumably, that package would here be called AddIntegers_jll.

Generating C++ Code

To write the generated C++ code, use code:

using AddIntegers

For convenience, the generated C++ code also contains the generated Julia code as comments. This code is not used anywhere, but helps understand the generated code.

See the package STL.jl, where such a script is used in deps/build.jl.

Modifying Input and Output types

In many cases, either the input types of a wrapper function or the result should be processed on the Julia side before being passed to the C++ wrapper function. For example, the Julia add_int function above expects arguments of type Cint, and that might be inconvenient -- we might prefer it to accept arguments of type Integer that are automatically converted to Cint. Similarly, it might be convenient to convert the result type Cint to Int.

Such code can also be generated automatically:

eval(cxxfunction(FnName(:add_int, "AI_add_int", libAddIntegers),
                 FnResult(Cint, "int", Int, expr -> :(convert(Int, $expr))),
                 [FnArg(:x, Cint, "x", "int", Integer, identity),
                  FnArg(:y, Cint, "y", "int", Integer, identity)],
                 "return add_int(x, y);"))

The two extra arguments to FnResult and FnArg describe the final output type and initial input type, respectively, as well as a conversion function. This conversion function acts on Julia expressions while the Julia code is generated; it is not a function that is applied at run time. The generated Julia function is then

function add_int(x::Integer, y::Integer)
    res = ccall(("add_int", libAddIntegers), Cint, (Cint, Cint),
                convert(Cint, x), convert(Cint, y))
    return convert(Int, res)

The generated C++ code is unaffected.

Generic Functions

It is not possible to generate C++ functions at run time. This means that all types need to be known at compile time; it is not possible to define a generic (parameterized) wrapper function. However, it is possible to generate a series of wrapper functions in a loop, as in:

types = Set([Int8, Int16, Int32, Int64])
for T in types
    CT = cxxtype[T]    # Find C++ type for T
    NT = cxxname(CT)   # Generate C++ identifier for CT
    eval(cxxfunction(FnName(:add, "add_$CT", libAddIntegers),
                     FnResult(T, CT),
                     [FnArg(:x, T, "x", CT),
                      FnArg(:y, T, "y", CT)],
                     "return x + y;"))

This generates a Julia function add with four methods, one for each integer type. Note that a single Julia function can have multiple methods (if the argument types differ), while the generated wrapper functions use C linkage and thus cannot use overloading. We thus use the type as prefix to the C++ wrapper function name.

Real-World Examples

The package STL.jl wraps the C++ STL types std::map, std::shared_ptr, and std::vector via CxxInterface.jl.