Calling C++ Functions from Julia#

We’ve seen how to call Julia functions from C++. Despite being more of a Julia-wrapper for C++ than a C++-wrapper for Julia, in jluna, calling C++ functions from Julia is actually just as convenient and performant.

To call a C++ function, we need to assign to a Julia-side variable, a lambda.

C++ Hint: Lambdas are C++s anonymous function objects. Before continuing with this section, it is recommended to read up on the basics of lambdas here. Users are expected to know about basic syntax, trailing return types and capture clauses from this point onward.

Creating a Function Object#

Let’s say we have the following, simple lambda:

auto add = [](Int64 a, Int64 b) -> Int64 
    return a + b;

This function has the signature (Int64, Int64) -> Int64.

When interfacing with jluna, we should always manually specify the trailing return type of lambda using ->. We should never use auto, either for the lambdas return- or any of the argument-types.

To make this function available to Julia, we use as_julia_function.

  • the argument of as_julia_function is a lambda or std::function object

  • the template argument of as_julia_function is the functions signature

C++ Hint: std::function is a class able to wrap any function in a movable object. See the official documentation for more details.

Because add has the signature (Int64, Int64) -> Int64, we use as_julia_function<Int64(Int64, Int64)>.

C++ Hint: std::function and thus as_julia_function uses the C-style syntax for a functions’ signature. A function with return-type R and argument types T1, T2, ..., Tn has the signature (T1, T2, ..., Tn) -> R, or R(T1, T2, ..., Tn) in C-style.

We can then assign the result of as_julia_function to a Julia variable like so:

// declare lambda
auto add = [](Int64 a, Int64 b) -> Int64 
    return a + b;

// bind to Julia-side variable
Main.create_or_assign("add", as_julia_function<Int64(Int64, Int64)>(add));

From this point onwards, we can simply call the C++-side add by using the newly created Julia-side variable Main.add:

Main.safe_eval("println(add(1, 3))");

The return value of as_julia_function is a Julia-side object. This means, we can assign it to already existing proxies, or otherwise handle it like any other Julia-side value.

The Julia function can be called with Julia- or C++-side arguments, and its return value can be directly accessed from both Julia and C++.

Allowed Signatures#

Not all function signatures are supported for as_julia_function. Its argument (the C++ function) can only have one of the following signatures:

() -> T_r
(T1) -> T_r
(T1, T2) -> T_r
(T1, T2, T3) -> T_r


  • T_r is void or unboxable

  • T1, T2, T3 are boxable

This may seem limiting at first, how could we execute arbitrary C++ code when we are only allowed to use functions with a maximum of three arguments using only (Un)Boxable types? The next sections will answer this question.

Taking Any Number of Arguments#

Let’s say we want to write a function that takes any number of Strings and concatenates them. Obviously, just 3 arguments are not enough for this. Luckily, there is a workaround. Instead of using a n-argument function, we can use a 1-argument function where the argument is a n-element vector:

// declare lambda, jluna::Array (aka. Base.Array) as argument
auto concat_all = [](jluna::Array<std::string, 1> arg) -> std::string
    // append through stringstream
    std::stringstream str;
    for (std::string s : arg)
        str << s;
    str << std::endl;
    // return string
    return str.str();

C++ Hint: std::stringstream is a stream that we can write strings into using operator<<. We then flush it using std::endl, and convert its contents to a single std::string using the member function .str(). More info about std::stringstream can be found in the C++ manual.

This lambda has the signature

  • (jluna::Array<std::string, 1>) -> std::string

Because jluna::Array<T, N> boxes into Base.Array{T, N}, Julia-side, the resulting function will have the signature

  • (Base.Array{String, 1}) -> String

Therefore, we move it Julia-sie using as_julia_function like so:

// create new variable
    "concat_all",           // variable name
    as_julia_function<      // as_julia_function call
        std::string(jluna::Array<std::string, 1>) // C++ signature
    >(concat_all)           // lambda

We can then call concat_all Julia-side, with any number of arguments, by wrapping the arguments in a Julia-side vector:

    println(concat_all(["GA", "TT", "AC", "A"]))

If we want to truly call it with any number of arguments, not just a vector, we can simply do:

// declare lambda
auto concat_all = [](jluna::Array<std::string, 1> arg) -> std::string
    // ...

// bind lambda to `concat_all_aux`
    "concat_all_aux",   // now named concat_all_aux
    as_julia_function<std::string(jluna::Array<std::string, 1>)>(concat_all)

// create new proper Julia function `concat_all`
// that forwards its n arguments as a 
// n-sized vector to `concat_all_aux`
    concat_all(xs::String...) = concat_all_aux(String[xs...])
// can now be called with n arguments
    println(concat_all("now ", "callable ", "like ", "this"))
now callable like this

Where we renamed the object holding the C++ lambda concat_call_aux, then called that object using a Julia-method with signature (::String...) -> String, which forwards its arbitrary number of arguments as a vector to concat_call_aux, thus achieving the desired syntax.

If we want our lambda to take any number of differently-typed arguments, we can either wrap them in a jluna::ArrayAny1d (which has the value type Any and thus can contain elements of any type), or we can use a std::tuple, both of which are (Un)Boxable. The latter should be preferred for performance reasons.

Using Non-Julia Objects#

We now know how to work around the restriction on the number of arguments, but what about the types? Not all types are (Un)Boxable, but this does not mean we cannot use arbitrary C++ types. How? By using captures.

Let’s say we have the following C++ class:

// declare non-Julia class
struct NonJuliaObject
    // member
    Int64 _value;
    // ctor
    NonJuliaObject(Int64 in)
        : _value(in)
    // member function: doubles _value n-times
    void double_value(size_t n)
        for (size_t i = 0; i < n; ++i)
            _value = 2 * _value;

// instance the class C++-side
auto instance = NonJuliaObject(13);

This object is obviously not (Un)Boxable.

The naive approach to modifying instance would be with the following lambda:

auto modify_instance = [](NonJuliaObject& instance, size_t n) -> void

This lambda has the signature (NonJuliaObject&, size_t) -> void, which is a disallowed signature because NonJuliaObject& is not boxable.

Instead of handing instance to the lambdas function body through an argument, we can instead forward it through its capture:

auto modify_instance = [instance_ref = std::ref(instance)] (size_t n) -> void

C++ Hint: std::ref is used to create a reference wrapper around any instanced object. It is very similar to Julia’s Base.Ref in functionality. To “unwrap” it, we use .get() in C++, where we would use [] in Julia.

Lambda syntax can get quite complicated, so let’s talk through this step-by-step.

Firstly, this second lambda has the signature (size_t) -> void. Capture variables do not affect a lambdas signature.

Inside the capture [], we have the expression instance_ref = std::ref(instance). This expression creates a new variable, instance_ref, that will be available inside the lambdas body. We initialize instance_ref with std::ref(instance), which creates a reference wrapper around our desired C++-side instance. A reference wrapper acts the same as a plain reference in terms of memory ownership, as long as the reference wrapper stays in scope, instance will too. Therefore, as long as the lambda body stays in scope, so will instance_ref and therefore instance.

Having captured instance through the reference wrapper, we can modify it inside our body by first unwrapping it using .get(), then applying whatever mutation we intend to. In our case, we are calling double_value with the size_t argument of the lambda.

After all this wrapping, we can simply:

// declare instance
auto instance = NonJuliaObject(13);

// declare lambda
auto modify_instance = 
    [instance_ref = std::ref(instance)] (size_t n) -> void

// create Julia-side variable
    "modify_instance",  // variable name
    as_julia_function<void(size_t)>(modify_instance)  // lambda

// call function Julia-side

// print value of C++-side instance
std::cout << instance._value << std::endl;

The Julia-side function modified our C++-side instance, despite its type being uninterpretable to Julia.

By cleverly employing captures and collections / tuples, the restriction on what functions can be forwarded to Julia using as_julia_function are lifted. Any arbitrary C++ function (and thus any arbitrary C++ code) can now be executed Julia-side. Furthermore, calling C++ functions like this introduces no overhead, making this feature of jluna very powerful.