2026-08-13 –, Room 3
Multi-GPU execution is the future - as data sizes grow, and as more work is pushed to the GPU, a single GPU no longer suffices. Unfortunately, programming an algorithm for multi-GPU is more complicated than single-GPU - you now have to deal with the complexity of multi-device data movement and multi-stream synchronization, which puts more burden on the algorithm author and takes away from just writing the algorithm in the simplest, most readable manner. Thankfully, Dagger.jl makes programming multi-GPU algorithms much easier with its Datadeps framework, which lets you focus on writing the algorithm at a high level while Dagger handles the details of managing multiple GPUs.
This talk will explain the problems around multi-GPU programming, and show how Dagger handles them. We will show how the Datadeps framework makes it much easier to write algorithms which naturally support multi-GPU execution, and show the tools that Dagger and Datadeps provide to make algorithm design a breeze.
Julian is a Research Software Engineer at MIT's JuliaLab, where he focuses on improving Julia's support for HPC and GPU computing. Julian has previously authored and maintained the AMDGPU.jl package (for programming AMD's GPUs from Julia), and now focuses his efforts on maintaining and developing the Dagger.jl package, to improve the state of productive parallel programming.
Consultant at MIT's JuliaLab, Co-maintainer of Dagger. My interests span from more broad topics such as the accessibility and educational initiatives for parallel computing to Applied Physics and Numerical Linear Algebra.