RustaCUDA

RustaCUDA helps you bring GPU-acceleration to your projects by providing a flexible, easy-to-use interface to the CUDA GPU computing toolkit. RustaCUDA makes it easy to manage GPU memory, transfer data to and from the GPU, and load and launch compute kernels written in any language.

Table of Contents

• Table of Contents
  • Goals
  • Roadmap
  • Quickstart
  • Contributing
  • Maintenance
  • License
  • Requirements
  • Related Projects

Goals

The primary design goals are:

• High-Level: Using RustaCUDA should feel familiar and intuitive for Rust programmers.
• Easy-to-Use: RustaCUDA should be well-documented and well-designed enough to help novice GPU programmers get started, while not limiting more experienced folks too much.
• Safe: Many aspects of GPU-accelerated computing are difficult to reconcile with Rust's safety guarantees, but RustaCUDA should provide the safest interface that is reasonably practical.
• Fast: RustaCUDA should aim to be as fast as possible, where it doesn't conflict with the other goals.

RustaCUDA is intended to provide a programmer-friendly library for working with the host-side CUDA Driver API. It is not intended to assist in compiling Rust code to CUDA kernels (though see rust-ptx-builder for that) or to provide device-side utilities to be used within the kernels themselves.

RustaCUDA is deliberately agnostic about how the kernels work or how they were compiled. This makes it possible to (for example) use C kernels compiled with nvcc.

Roadmap

RustaCUDA currently supports a minimum viable subset of the CUDA API (essentially, the minimum necessary to manage memory and launch basic kernels). This does not include:

• Any asynchronous operation aside from kernel launches
• Access to CUDA 1/2/3D arrays and texture memory
• Multi-GPU support
• Runtime linking
• CUDA Graphs
• And more!

These additional features will be developed later, as time permits and as necessary. If you need a feature that is not yet supported, consider submitting a pull request!

Quickstart

Before using RustaCUDA, you must install the CUDA development libraries for your system. Version 8.0 or newer is required. You must also have a CUDA-capable GPU installed with the appropriate drivers.

First, set the CUDA_LIBRARY_PATH environment variable to the location of your CUDA headers:

export CUDA_LIBRARY_PATH="C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.1\lib\x64"

Some Ubuntu users have encountered linker errors when using CUDA_LIBRARY_PATH. If you see an error like this:

  = note: /usr/bin/ld: cannot find -lcudart                                                              
          /usr/bin/ld: cannot find -lcublas                                                              
          collect2: error: ld returned 1 exit status 

Using LIBRARY_PATH instead of CUDA_LIBRARY_PATH seems to help.

Now, to start building a basic CUDA crate. Add the following to your Cargo.toml:

[dependencies]
rustacuda = "0.1"
rustacuda_core = "0.1"
rustacuda_derive = "0.1"

And this to your crate root:

#[macro_use]
extern crate rustacuda;

#[macro_use]
extern crate rustacuda_derive;
extern crate rustacuda_core;

Next, download the resources/add.ptx file from the RustaCUDA repository and place it in the resources directory for your application.

The examples/ directory contains sample code that helps getting started. To execute the most simple example, (adding two numbers on GPU), place this code to your main.rs file.

#[macro_use]
extern crate rustacuda;

use rustacuda::prelude::*;
use rustacuda::memory::DeviceBox;
use std::error::Error;
use std::ffi::CString;

fn main() -> Result<(), Box<dyn Error>> {
    // Initialize the CUDA API
    rustacuda::init(CudaFlags::empty())?;
    
    // Get the first device
    let device = Device::get_device(0)?;

    // Create a context associated to this device
    let context = Context::create_and_push(
        ContextFlags::MAP_HOST | ContextFlags::SCHED_AUTO, device)?;

    // Load the module containing the function we want to call
    let module_data = CString::new(include_str!("../resources/add.ptx"))?;
    let module = Module::load_from_string(&module_data)?;

    // Create a stream to submit work to
    let stream = Stream::new(StreamFlags::NON_BLOCKING, None)?;

    // Allocate space on the device and copy numbers to it.
    let mut x = DeviceBox::new(&10.0f32)?;
    let mut y = DeviceBox::new(&20.0f32)?;
    let mut result = DeviceBox::new(&0.0f32)?;

    // Launching kernels is unsafe since Rust can't enforce safety - think of kernel launches
    // as a foreign-function call. In this case, it is - this kernel is written in CUDA C.
    unsafe {
        // Launch the `sum` function with one block containing one thread on the given stream.
        launch!(module.sum<<<1, 1, 0, stream>>>(
            x.as_device_ptr(),
            y.as_device_ptr(),
            result.as_device_ptr(),
            1 // Length
        ))?;
    }

    // The kernel launch is asynchronous, so we wait for the kernel to finish executing
    stream.synchronize()?;

    // Copy the result back to the host
    let mut result_host = 0.0f32;
    result.copy_to(&mut result_host)?;
    
    println!("Sum is {}", result_host);

    Ok(())
}

If everything is working, you should be able to run cargo run and see the output:

Sum is 30.0

Contributing

Thanks for your interest! Contributions are welcome.

Issues, feature requests, questions and bug reports should be reported via the issue tracker above. In particular, because RustaCUDA aims to be well-documented, please report anything you find confusing or incorrect in the documentation.

Code or documentation improvements in the form of pull requests are also welcome. Please file or comment on an issue to allow for discussion before doing a lot of work, though.

For more details, see the CONTRIBUTING.md file.

Maintenance

RustaCUDA is currently maintained by Brook Heisler (@bheisler).

License

RustaCUDA is dual-licensed under the Apache 2.0 license and the MIT license.

Requirements

RustaCUDA requires at least CUDA version 8 to be installed.

Related Projects

• accel is a full CUDA computing framework. Thanks to accel for creating and maintaining the cuda-sys FFI wrapper library.
• rust-ptx-builder is a build.rs helper library which makes it easy to compile Rust crates into CUDA kernels.