07-20, 14:00–17:00 (UTC), Green
In this workshop, we will demonstrate three major packages for programming GPUs in Julia (CUDA.jl, AMDGPU.jl, oneAPI.jl), and the different programming models, tools and APIs that these packages support.
Julia has several packages for programming GPUs, each of which support various programming models. In this workshop, we will demonstrate the use of three major GPU programming packages: CUDA.jl for NVIDIA GPUs, AMDGPU.jl for AMD GPUs, and oneAPI.jl for Intel GPUs. We will explain the various approaches for programming GPUs with these packages, ranging from generic array operations that focus on ease-of-use, to hardware-specific kernels for when performance matters.
Most of the workshop will be vendor-neutral, and the content will be available for all supported GPU back-ends. There will also be a part on vendor-specific tools and APIs.
Attendees will be able to follow along, but are recommended to have access to a suitable GPU for doing so. Material for this workshop can be found at https://github.com/maleadt/juliacon21-gpu_workshop
PhD Student at MIT
I'm a software engineer at Julia Computing, working on Julia's GPU packages and compilers.
I am an HPC software engineer working at the JuliaLab. I maintain Dagger.jl, AMDGPU.jl, and BPFnative.jl, and generally enjoy the challenge of hacking on compilers and HPC runtimes.