Introducing CubeCL, a new project that modernizes GPU computing, making it easier to write optimal and portable kernels. CubeCL allows you to write GPU kernels using a subset of Rust syntax, with ongoing work to support more language features.

Why it Matters

CubeCL tackles three major challenges in GPU computing

- Portability: The same codebase can be used to program any GPU without a loss in performance.

- Usability: No need for a new shader language — simply add an attribute on top of your Rust code and voilà, it can now run on any GPU.

- Performance: We generate fine-grained kernel specialization via an innovative compile-time system to use the most efficient instructions available.

How it works

CubeCL leverages Rust's proc macro system in a unique two-step process:

1. Parsing: The proc macro parses the GPU kernel code using the syn crate.

2. Expansion: Instead of immediately generating an Intermediate Representation (IR), the macro generates a new Rust function.

The generated function, semantically similar to the original, is responsible for creating the IR when called. This approach differs from traditional compilers, which typically generate IR directly after parsing. Our method enables several key features:

- Comptime: CubeCL functions can contain sections marked as Comptime. These sections are executed during compilation rather than at runtime. This allows for the creation of highly specialized kernels by incorporating compile-time information directly into the generated code.

- Automatic Vectorization: By simply vectorizing the inputs of a CubeCL function, we can determine the vectorization factor of each intermediate variable during the expansion.

- Rust Integration: The generated code remains valid Rust code, allowing it to be bundled without any dependency on the specific runtime.

Our goal extends beyond providing an optimized compute language; we aim to develop an ecosystem of high-performance and scientific computing in Rust.For now we have highly optimized matrix multiplication kernels, leveraging Tensor Cores on NVIDIA's hardware when available. We are going to focus on adding more algorithms, but community contributions are more than welcome. There is still a lot of work to be done!

Don't hesitate to check the repo and ask any questions that come to mind.

Available (and planned) runtimes: https://github.com/tracel-ai/cubecl?tab=readme-ov-file#runti...