2026-08-14 –, Room 5
Round-table open discussion of everything about Dagger.jl. Success or failure stories, gripes and joys, ideas for new features, discussion of existing bugs or missing documentation, and more!
Dagger.jl is a rising star in the landscape of High Performance Computing, striving to make parallel computing easy and productive for everyone. Dagger has grown significantly over the last few years, and many more improvements are already planned for 2026.
But during this BoF, we want to hear from you, the community, to understand why you do (or don't) use Dagger to solve your problems, and how Dagger can do better. We welcome both positive feedback and negative constructive criticism, and would like to find out what you want to see change in Dagger in 2026, 2027, and beyond! We'll also cover some of the new features and benchmarks of Dagger so you can see what new things have dropped since 2025.
We welcome past and current users of Dagger, and also those just interested in sitting in to learn more about Dagger.
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.