The State of GPGPU in Rust

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At work a few months ago, we started experimenting with GPU-acceleration. My boss asked if I was interested. I didn’t know anything about programming GPUs, so of course I said “Heck yes, I’m interested!“. I needed to learn about GPUs in a hurry, and that led to my GPU Path Tracer series. That was a lot of fun, but it showed me that CUDA support in Rust is pretty poor. If our experiments ever turn into an actual product, I would have to recommend we write the GPU code in C.

To spare myself and others that dreadful fate, I decided to work towards making Rust’s GPGPU story as good as C’s. The first step was to survey the landscape to see what’s out there.


I started by looking at OpenCL crates. I found two - rust-opencl and ocl. rust-opencl is abandoned, so I’ll focus on ocl.

I’m not very familiar with OpenCL, but ocl looks pretty solid to me. It provides Rustic abstractions over the OpenCL C API, but allows the programmer to drop down to the lower level if needed. OpenCL in Rust is already as good as it is in C. OpenCL works on AMD GPUs as well as NVIDIA ones, which is a nice bonus.

I don’t much like OpenCL, though. OpenCL kernels are written in OpenCL C, and the source code is passed to the GPU driver for compilation at runtime. I want to avoid writing C. Further, the runtime compilation model and change of language means that we lose all of the nice compile-time safety checks that Rust provides.


Despite being NVIDIA-only, CUDA seems to be more widely used than OpenCL, and it’s not hard to see why. It provides an easy, single-source approach to GPGPU - you write C or C++ and mark GPU code with special annotations. Compile the code with a compiler-wrapper called nvcc, and then you can launch kernels almost as easily as calling a function. The library APIs are well-designed and intuitive for C programmers. I used CUDA for my path tracer series. CUDA support in Rust was pretty rough, and it’s hasn’t gotten better.

CUDA in Rust should be just as smooth as it is in C. rustc already supports LLVM’s NVPTX backend. You can write Rust code, mark it with some procedural macros and execute it on the GPU. You can share structure definitions and functions between the CPU and the GPU, the compiler provides all of its usual compile-time checks, and it all works smoothly, right?

Well, no. The NVPTX backend is right at the bottom of Tier 3 support with a lot of asterisks. To compile to PTX, you have to use a specific nightly build (2018-04-10) and you have to use Xargo to cross-compile the core library. You have to install a bunch of extra LLVM tools to link together different crates (which may involve compiling them from source). Once you fight through all of that, rustc frequently produces an invalid PTX file or just crashes and you have to guess why. It’s… not great.

accel is still best-in-class here, but that’s not saying much. They’ve forked the Rust compiler to improve PTX support and made a tool to install their custom compiler into your rustup toolchains. Unfortunately, that tool only works on Linux. The documentation is poor, and even getting it to work on Linux requires digging into the source code to decipher mysterious error messages.

There are Rust bindings for many CUDA libraries like CuBLAS, but these are also abandoned.

CUDA in Rust needs a lot of work to catch up to CUDA in C.

Higher-Level Libraries

There are a number of libraries seeking to provide higher-level interfaces to the GPU.

The oldest is Collenchyma, which came out of Leaf AI and focuses on neural networks. It was completely abandoned along with Leaf. A fork called Parenchyma was created, which changed a lot of Collenchyma’s API and claims to be under active development. There hasn’t been a Git commit in six months. It’s probably abandoned as well, and remaining users are unable to compile it on the latest nightly compiler builds.

The other big one is arrayfire-rust, which is a Rust binding to ArrayFire. This is attached to ArrayFire LLC, so it has some corporate backing and probably won’t be totally abandoned. Unlike Parenchyma, it has some activity in the last few months. ArrayFire provides the ability to create and fill arrays of values and then apply pre-baked operations to them. If you want lower-level control to get that last bit of performance, or if your problem doesn’t fit their model, then I think you’re out of luck. I’m skeptical of claims that it’s portable across OpenCL, CUDA and CPUs. The performance characteristics of CPUs are so different from those of GPUs that it will be difficult to get optimal performance on both.

ArrayFire in Rust is at least as good as ArrayFire in C, but that’s about all I can say. There is nothing similar to Thrust or OpenACC yet.

Other Rust GPGPU Projects

Vulkano is a Rust interface to the Vulkan graphics API. It supports compute shaders as well. All of my concerns with OpenCL apply here as well - it uses a special C-like language (similar to the one OpenCL uses) for the shaders rather than plain Rust. The surrounding API is quite verbose as well. I think Vulkan is primarily focused on graphics, and compute-shaders are provided as an extension to that.

I’d also like to mention rlsl - Rust-Like Shading Language. This compiles a subset of Rust to Vulkan SPIR-V. It’s an interesting project, but the README warns that it is not production-ready and does not accept contributions yet. This too is focused on writing shaders for graphics rather than general-purpose computation.


If you like OpenCL, Vulkan or ArrayFire, all of them have excellent Rust bindings. On the other hand, CUDA in Rust is simply not ready for use in production. Rust has no alternative for many other GPGPU tools that C/C++ programmers have, like Thrust or OpenACC.

GPGPU is an important use-case for a low-level, high-performance language like Rust. It’s relevant to a number of fields, including machine learning, cryptography, cryptocurrency, image-processing, physical simulations, and scientific computing.

I want to work to improve this situation. I think the CUDA model of writing host and device code in the same language is valuable, so that’s what I’ll start with. This will involve working with the Rust compiler team and contributing improvements to rustc, maybe even LLVM. Aside from some bug reports I’ve never done that before, and it would help to have a mentor. If you’d be willing to answer questions like “what would have to happen before NVPTX could be a Tier 2 backend” or “how do I get this build system to work on Windows”, please send me an email or post a comment.