Julia 中文文档

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Julia 嵌入式

# 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

Note: This section covers embedding Julia code in C on Unix-like operating systems. For doing
this on Windows, please see the section following this.

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 */

    /* run Julia commands */

    /* strongly recommended: notify Julia that the
         program is about to terminate. this allows
         Julia time to cleanup pending write requests
         and run all finalizers
    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.

!!! note
Currently, dynamically linking with the libjulia shared library requires passing the RTLD_GLOBAL
option. In Python, this looks like:

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

!!! note
If the julia program needs to access symbols from the main executable, it may be necessary to
add -Wl,--export-dynamic linker flag at compile time on Linux in addition to the ones generated
by julia-config.jl described below. This is not necessary when compiling a shared library.

# 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[])
    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:

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.

# High-Level Embedding on Windows with Visual Studio

If the JULIA_DIR environment variable hasn't been setup, add it using the System panel before
starting Visual Studio. The bin folder under JULIA_DIR should be on the system PATH.

We start by opening Visual Studio and creating a new Console Application project. To the 'stdafx.h'
header file, add the following lines at the end:

#include <julia.h>

Then, replace the main() function in the project with this code:

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

    /* run Julia commands */

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

The next step is to set up the project to find the Julia include files and the libraries. It's important to
know whether the Julia installation is 32- or 64-bits. Remove any platform configuration that doesn't correspond
to the Julia installation before proceeding.

Using the project Properties dialog, go to C/C++ | General and add $(JULIA_DIR)\include\julia\ to the
Additional Include Directories property. Then, go to the Linker | General section and add $(JULIA_DIR)\lib
to the Additional Library Directories property. Finally, under Linker | Input, add libjulia.dll.a;libopenlibm.dll.a;
to the list of libraries.

At this point, the project should build and run.

# 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

# 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)");
// Do something with ret

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)

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.

jl_gc_collect()Force a GC run
jl_gc_enable(0)Disable the GC, return previous state as int
jl_gc_enable(1)Enable the GC, return previous state as int
jl_gc_is_enabled()Return current state as int

# 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

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:


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

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.