Viewing profile — nathanielsimard

I think it will be cost effective at some point. Computers were limited to research institutes before the personal computer arrived.

CubeCL supports WebGPU and can be used with wasm!

I don't recall the reason why, point is a valid name.

Well we can agree to disagree, CubeCL also has the concept of instruction parallelism, which would be used to target simd instructions on CPU. Our algorithms are normally flexible …

Using the naming from one of the existing API would put too much bias towards that API. It started as a WebGPU project early on, but some features are not present so mixing terms w…

One of the author here, don't hesitate if you have any question or comment!

We have safe and unsafe version for launching kernels where we can ensure that a kernel won't corrupt data elsewhere (and therefore won't create memory error or segfaults). But wit…

The need to build CubeCL came from the Burn deep learning framework ( https://github.com/tracel-ai/burn ), where we want to easily build algorithms like in CUDA with a real program…

We support warp operations, barriers for Cuda, atomics for most backends, tensor cores instructions as well. It's just not well documented on the readme!

One of the main author here, the readme isn't really well up-to-date. We have our own gemm implementation based on CubeCL. It's still moving a lot, but we support tensor cores, use…

A lot of things happen at compile time, but you can execute arbitrary code in your kernel that executes at compile time, similar to generics, but with more flexibility. It's very n…

During the last iteration of CubeCL, we refactored the matrix multiplication GPU kernel to work with many different configurations and element types. The goal was to improve perfor…

Burn is now the first fully Rust-native deep learning framework. Do everything in Rust, from GPU kernels to model definition. No CUDA, C++ or WGSL needed thanks to CubeCL that we r…

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 Ru…

Happy to share what we have been working on lately. The blog post explores Burn's tensor operation stream strategy, optimizing models through an eager API by creating custom kernel…

This release is packed with new features and improved performance, but the major focus was on enhancing the user API and the documentation. We updated the API to remove instances w…

When developing our WebGPU backend for the Burn deep learning framework, we faced numerous challenges in optimizing the execution speed. Autotune serves as our solution to the chal…