Embedding Julia

As we have seen in Calling C and Fortran Code, Julia has a simple and efficient way to call functions written in C. But there are situations where the opposite is needed: calling Julia function from C code. This can be used to integrate Julia code into a larger C/C++ project, without the need to rewrite everything in C/C++. Julia has a C API to make this possible. As almost all programming languages have some way to call C functions, the Julia C API can also be used to build further language bridges (e.g. calling Julia from Python or C#).

High-Level Embedding

We start with a simple C program that initializes Julia and calls some Julia code:

#include <julia.h>
JULIA_DEFINE_FAST_TLS() // only define this once, in an executable (not in a shared library) if you want fast code.

int main(int argc, char *argv[])
{
    /* required: setup the Julia context */
    jl_init();

    /* run Julia commands */
    jl_eval_string("print(sqrt(2.0))");

    /* strongly recommended: notify Julia that the
         program is about to terminate. this allows
         Julia time to cleanup pending write requests
         and run all finalizers
    */
    jl_atexit_hook(0);
    return 0;
}

In order to build this program you have to put the path to the Julia header into the include path and link against libjulia. For instance, when Julia is installed to $JULIA_DIR, one can compile the above test program test.c with gcc using:

gcc -o test -fPIC -I$JULIA_DIR/include/julia -L$JULIA_DIR/lib test.c -ljulia $JULIA_DIR/lib/julia/libstdc++.so.6

Then if the environment variable JULIA_BINDIR is set to $JULIA_DIR/bin, the output test program can be executed.

Alternatively, look at the embedding.c program in the Julia source tree in the test/embedding/ folder. The file ui/repl.c program is another simple example of how to set jl_options options while linking against libjulia.

The first thing that has to be done before calling any other Julia C function is to initialize Julia. This is done by calling jl_init, which tries to automatically determine Julia's install location. If you need to specify a custom location, or specify which system image to load, use jl_init_with_image instead.

The second statement in the test program evaluates a Julia statement using a call to jl_eval_string.

Before the program terminates, it is strongly recommended to call jl_atexit_hook. The above example program calls this before returning from main.

>>> julia=CDLL('./libjulia.dylib',RTLD_GLOBAL)
>>> julia.jl_init.argtypes = []
>>> julia.jl_init()
250593296

Using julia-config to automatically determine build parameters

The script julia-config.jl was created to aid in determining what build parameters are required by a program that uses embedded Julia. This script uses the build parameters and system configuration of the particular Julia distribution it is invoked by to export the necessary compiler flags for an embedding program to interact with that distribution. This script is located in the Julia shared data directory.

Example

#include <julia.h>

int main(int argc, char *argv[])
{
    jl_init();
    (void)jl_eval_string("println(sqrt(2.0))");
    jl_atexit_hook(0);
    return 0;
}

On the command line

A simple use of this script is from the command line. Assuming that julia-config.jl is located in /usr/local/julia/share/julia, it can be invoked on the command line directly and takes any combination of 3 flags:

/usr/local/julia/share/julia/julia-config.jl
Usage: julia-config [--cflags|--ldflags|--ldlibs]

If the above example source is saved in the file embed_example.c, then the following command will compile it into a running program on Linux and Windows (MSYS2 environment), or if on OS/X, then substitute clang for gcc.:

/usr/local/julia/share/julia/julia-config.jl --cflags --ldflags --ldlibs | xargs gcc embed_example.c

Use in Makefiles

But in general, embedding projects will be more complicated than the above, and so the following allows general makefile support as well – assuming GNU make because of the use of the shell macro expansions. Additionally, though many times julia-config.jl may be found in the directory /usr/local, this is not necessarily the case, but Julia can be used to locate julia-config.jl too, and the makefile can be used to take advantage of that. The above example is extended to use a Makefile:

JL_SHARE = $(shell julia -e 'print(joinpath(Sys.BINDIR, Base.DATAROOTDIR, "julia"))')
CFLAGS   += $(shell $(JL_SHARE)/julia-config.jl --cflags)
CXXFLAGS += $(shell $(JL_SHARE)/julia-config.jl --cflags)
LDFLAGS  += $(shell $(JL_SHARE)/julia-config.jl --ldflags)
LDLIBS   += $(shell $(JL_SHARE)/julia-config.jl --ldlibs)

all: embed_example

Now the build command is simply make.

Converting Types

Real applications will not just need to execute expressions, but also return their values to the host program. jl_eval_string returns a jl_value_t*, which is a pointer to a heap-allocated Julia object. Storing simple data types like Float64 in this way is called boxing, and extracting the stored primitive data is called unboxing. Our improved sample program that calculates the square root of 2 in Julia and reads back the result in C looks as follows:

jl_value_t *ret = jl_eval_string("sqrt(2.0)");

if (jl_typeis(ret, jl_float64_type)) {
    double ret_unboxed = jl_unbox_float64(ret);
    printf("sqrt(2.0) in C: %e \n", ret_unboxed);
}
else {
    printf("ERROR: unexpected return type from sqrt(::Float64)\n");
}

In order to check whether ret is of a specific Julia type, we can use the jl_isa, jl_typeis, or jl_is_... functions. By typing typeof(sqrt(2.0)) into the Julia shell we can see that the return type is Float64 (double in C). To convert the boxed Julia value into a C double the jl_unbox_float64 function is used in the above code snippet.

Corresponding jl_box_... functions are used to convert the other way:

jl_value_t *a = jl_box_float64(3.0);
jl_value_t *b = jl_box_float32(3.0f);
jl_value_t *c = jl_box_int32(3);

As we will see next, boxing is required to call Julia functions with specific arguments.

Calling Julia Functions

While jl_eval_string allows C to obtain the result of a Julia expression, it does not allow passing arguments computed in C to Julia. For this you will need to invoke Julia functions directly, using jl_call:

jl_function_t *func = jl_get_function(jl_base_module, "sqrt");
jl_value_t *argument = jl_box_float64(2.0);
jl_value_t *ret = jl_call1(func, argument);

In the first step, a handle to the Julia function sqrt is retrieved by calling jl_get_function. The first argument passed to jl_get_function is a pointer to the Base module in which sqrt is defined. Then, the double value is boxed using jl_box_float64. Finally, in the last step, the function is called using jl_call1. jl_call0, jl_call2, and jl_call3 functions also exist, to conveniently handle different numbers of arguments. To pass more arguments, use jl_call:

jl_value_t *jl_call(jl_function_t *f, jl_value_t **args, int32_t nargs)

Its second argument args is an array of jl_value_t* arguments and nargs is the number of arguments.

Memory Management

As we have seen, Julia objects are represented in C as pointers. This raises the question of who is responsible for freeing these objects.

Typically, Julia objects are freed by a garbage collector (GC), but the GC does not automatically know that we are holding a reference to a Julia value from C. This means the GC can free objects out from under you, rendering pointers invalid.

The GC can only run when Julia objects are allocated. Calls like jl_box_float64 perform allocation, and allocation might also happen at any point in running Julia code. However, it is generally safe to use pointers in between jl_... calls. But in order to make sure that values can survive jl_... calls, we have to tell Julia that we hold a reference to a Julia value. This can be done using the JL_GC_PUSH macros:

jl_value_t *ret = jl_eval_string("sqrt(2.0)");
JL_GC_PUSH1(&ret);
// Do something with ret
JL_GC_POP();

The JL_GC_POP call releases the references established by the previous JL_GC_PUSH. Note that JL_GC_PUSH is working on the stack, so it must be exactly paired with a JL_GC_POP before the stack frame is destroyed.

Several Julia values can be pushed at once using the JL_GC_PUSH2 , JL_GC_PUSH3 , and JL_GC_PUSH4 macros. To push an array of Julia values one can use the JL_GC_PUSHARGS macro, which can be used as follows:

jl_value_t **args;
JL_GC_PUSHARGS(args, 2); // args can now hold 2 `jl_value_t*` objects
args[0] = some_value;
args[1] = some_other_value;
// Do something with args (e.g. call jl_... functions)
JL_GC_POP();

The garbage collector also operates under the assumption that it is aware of every old-generation object pointing to a young-generation one. Any time a pointer is updated breaking that assumption, it must be signaled to the collector with the jl_gc_wb (write barrier) function like so:

jl_value_t *parent = some_old_value, *child = some_young_value;
((some_specific_type*)parent)->field = child;
jl_gc_wb(parent, child);

It is in general impossible to predict which values will be old at runtime, so the write barrier must be inserted after all explicit stores. One notable exception is if the parent object was just allocated and garbage collection was not run since then. Remember that most jl_... functions can sometimes invoke garbage collection.

The write barrier is also necessary for arrays of pointers when updating their data directly. For example:

jl_array_t *some_array = ...; // e.g. a Vector{Any}
void **data = (void**)jl_array_data(some_array);
jl_value_t *some_value = ...;
data[0] = some_value;
jl_gc_wb(some_array, some_value);

Manipulating the Garbage Collector

There are some functions to control the GC. In normal use cases, these should not be necessary.

Working with Arrays

Julia and C can share array data without copying. The next example will show how this works.

Julia arrays are represented in C by the datatype jl_array_t*. Basically, jl_array_t is a struct that contains:

• Information about the datatype
• A pointer to the data block
• Information about the sizes of the array

To keep things simple, we start with a 1D array. Creating an array containing Float64 elements of length 10 is done by:

jl_value_t* array_type = jl_apply_array_type((jl_value_t*)jl_float64_type, 1);
jl_array_t* x          = jl_alloc_array_1d(array_type, 10);

Alternatively, if you have already allocated the array you can generate a thin wrapper around its data:

double *existingArray = (double*)malloc(sizeof(double)*10);
jl_array_t *x = jl_ptr_to_array_1d(array_type, existingArray, 10, 0);

The last argument is a boolean indicating whether Julia should take ownership of the data. If this argument is non-zero, the GC will call free on the data pointer when the array is no longer referenced.

In order to access the data of x, we can use jl_array_data:

double *xData = (double*)jl_array_data(x);

Now we can fill the array:

for(size_t i=0; i<jl_array_len(x); i++)
    xData[i] = i;

Now let us call a Julia function that performs an in-place operation on x:

jl_function_t *func = jl_get_function(jl_base_module, "reverse!");
jl_call1(func, (jl_value_t*)x);

By printing the array, one can verify that the elements of x are now reversed.

Accessing Returned Arrays

If a Julia function returns an array, the return value of jl_eval_string and jl_call can be cast to a jl_array_t*:

jl_function_t *func  = jl_get_function(jl_base_module, "reverse");
jl_array_t *y = (jl_array_t*)jl_call1(func, (jl_value_t*)x);

Now the content of y can be accessed as before using jl_array_data. As always, be sure to keep a reference to the array while it is in use.

Multidimensional Arrays

Julia's multidimensional arrays are stored in memory in column-major order. Here is some code that creates a 2D array and accesses its properties:

// Create 2D array of float64 type
jl_value_t *array_type = jl_apply_array_type(jl_float64_type, 2);
jl_array_t *x  = jl_alloc_array_2d(array_type, 10, 5);

// Get array pointer
double *p = (double*)jl_array_data(x);
// Get number of dimensions
int ndims = jl_array_ndims(x);
// Get the size of the i-th dim
size_t size0 = jl_array_dim(x,0);
size_t size1 = jl_array_dim(x,1);

// Fill array with data
for(size_t i=0; i<size1; i++)
    for(size_t j=0; j<size0; j++)
        p[j + size0*i] = i + j;

Notice that while Julia arrays use 1-based indexing, the C API uses 0-based indexing (for example in calling jl_array_dim) in order to read as idiomatic C code.

Exceptions

Julia code can throw exceptions. For example, consider:

jl_eval_string("this_function_does_not_exist()");

This call will appear to do nothing. However, it is possible to check whether an exception was thrown:

if (jl_exception_occurred())
    printf("%s \n", jl_typeof_str(jl_exception_occurred()));

If you are using the Julia C API from a language that supports exceptions (e.g. Python, C#, C++), it makes sense to wrap each call into libjulia with a function that checks whether an exception was thrown, and then rethrows the exception in the host language.

Throwing Julia Exceptions

When writing Julia callable functions, it might be necessary to validate arguments and throw exceptions to indicate errors. A typical type check looks like:

if (!jl_typeis(val, jl_float64_type)) {
    jl_type_error(function_name, (jl_value_t*)jl_float64_type, val);
}

General exceptions can be raised using the functions:

void jl_error(const char *str);
void jl_errorf(const char *fmt, ...);

jl_error takes a C string, and jl_errorf is called like printf:

jl_errorf("argument x = %d is too large", x);

where in this example x is assumed to be an integer.