Minisymposium Presentation

AMD GPU programming in Julia has seen significant improvements in performance and stability over the past year, transitioning from two disjoined runtime APIs to a single stack, improving the device support and adding new features.In this presentation, we demostrate key changes that have been made to the AMDGPU.jl package, which provides support for programming AMD GPUs, enabling a host of new applications.

To showcase the impact of these changes, we present state-of-the-art real-time neural rendering algorithms developed entirely in Julia in a backend-agnostic manner.Given a set of images, these algorithms reconstruct an environment in a matter of minutes and allow the user to interact with it during training and evaluation.To achieve real-time performance, these implementations incorporate optimized hand-written GPU kernels that integrate with Automatic Differentiation systems, offering a high-level interface without sacrificing performance.

Simplicity of implementation, seamless support for multiple backends, and real-time performance of these algorithms position the Julia language as a strong candidate for high-performance computing.