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README.md

jlrs

Build Status Coverage Status License:MIT

The main goal behind jlrs is to provide a simple and safe interface to the Julia C API. Using this crate you can call arbitrary Julia code from Rust, including your own, and share data between the two languages. Currently this crate has only been tested on Linux, if you try to use it on another OS it will likely fail to generate the bindings to Julia.

Generating the bindings

This crate depends on jl-sys which contains the raw bindings to the Julia C API, these are generated by bindgen. The recommended way to install Julia is to download the binaries from the official website, which is distributed in an archive containing a directory called julia-x.y.z. This directory contains several other directories, including a bin directory containing the julia executable.

In order to ensure the julia.h header file can be found, you have to set the JL_PATH environment variable to /path/to/julia-x.y.z. Similarly, in order to load libjulia.so you must add /path/to/julia-x.y.z/lib to the LD_LIBRARY_PATH environment variable.

Using this crate

The first thing you should do is use the prelude-module with an asterisk, this will bring all the structs and traits you're likely to need in scope. Before you can use Julia it must first be initialized. You do this by creating a Runtime with Runtime::new, this method forces you to pick a stack size. You will learn how to choose this value in the section about memory management. Note that this method can only be called once, if you drop the Runtime you won't be able to create a new one and have to restart the entire program.

With the Runtime you can do two things: you can call Runtime::include to include your own Julia code, and Runtime::session to interact with Julia. If you want to create arrays with more than three dimensions or borrow arrays with more than one, you should include jlrs.jl first, which you can find in the root of this crate's github repository. This is necessary because this functionality currently depends on some Julia code defined in that file. The latter method takes a closure with a single argument, a mutable reference to a Session. Using this Session you can do useful things inside the closure.

In order to actually call a function, you need three things:

  • A place to store the function output that's protected from garbage collection
  • Arguments to call the function with, also protected from garbage collection
  • A handle to the function

The Session lets you take care of all the preliminary work; with Session::new_unassigned you create a safe place for the output to go, while other methods like Session::new_primitive, Session::new_string and Session::new_owned_array let you transfer primitive datatypes like u8 and f32, strings, and n-dimensional arrays to Julia. For a full overview of the possibilities, you should take a look at the documentation for Session.

In the case of named functions, ie those defined inside a module, you must first acquire a handle to that module. You can can a handle to the Main, Base and Core modules with the methods Session::main_module, Session::base_module and Session::core_module respectively. You can traverse the path to a deeper module with Module::submodule. Finally, you get a handle to a function with Module::function. Because these are global handles they don't need to be protected from garbage collection.

There's something a bit special about functions though: there's no real way to differentiate between functions and other globals in a module. For example, there's nothing that prevents you from calling Base.pi as a function. It's not possible to check if you call functions with the correct arguments, either. It's up to you to ensure you call things correctly. Failing to do so will only result in an error being returned, though, rather than crash your program.

With all these things in hand, it is time to call Session::execute. This method works just like Runtime::session does: it takes closure with a single argument, a mutable reference to an ExecutionContext. Besides letting you copy data from Julia to Rust with ExecutionContext::try_unbox, you will need this reference when calling functions using the Call trait.

Both Runtime::session and Session::execute have generic return types, which lets you easily return the results of your computations. As a simple example, this is how you can add two numbers:

use jlrs::prelude::*;

fn main() {
    let mut runtime = unsafe { Runtime::new(16).unwrap() };

    let output = runtime.session(|session| {
        let output = session.new_unassigned()?;
        let i = session.new_primitive(2u64)?;
        let j = session.new_primitive(1u32)?;

        session.execute(|exec_ctx| {
            let add = exec_ctx.base_module().function("+")?;
            let result = add.call2(exec_ctx, output, i, j)?;
            exec_ctx.try_unbox::<u64>(&result)
        })
    }).unwrap();

    assert_eq!(output, 3);
}

Memory management

So far you've seen that you can use a Session to allocate data and an ExecutionContext to use that data. The data allocated using a Session is valid until the session ends and nothing prevents you from allocating more data and calling Session::execute again. The actual allocations happen when Session::execute is called. If nothing was allocated, calling this function will take one slot on the stack, otherwise it will take as many slots as allocations plus three.

It's also possible to allocate temporary data with Session::with_temporaries, which works mostly the same way as Runtime::session and Session::execute do, except its argument is an AllocationContext rather than a mutable reference to one. The AllocationContext offers you the same interface as Session does, with two major differences:

  • AllocationContext::execute takes the context by value rather than by reference, you have to stop using the AllocationContext after calling AllocationContext::execute.
  • Data allocated by an AllocationContext is only valid within that context, rather than the entire session.

So, to summarize, in order to estimate how large your stack size should be, you need to check where you call Session::execute, Session::with_temporaries and AllocationContext::execute and figure out how many items you're allocating to get a rough estimate for how many slots you need. In case your computations fail due to exceeding the stack size, you can use Runtime::set_stack_size to create a larger one.

Limitations

Calling Julia is entirely single-threaded. You won't be able to use the Runtime from another thread and while Julia is doing stuff you won't be able to interact with it. Support for multithreading in Julia is currently in an experimental phase, there might still be options to use this functionality in order to build experimental support for some kind of multithreaded task-like system, but that has not been investigated yet.

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