2025-07-25 – 16:00-17:00 (Africa/Abidjan), Main Room 4
Round-table discussion of everything about Dagger.jl. Success or failure stories, 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 2025.
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 2025, 2026, and beyond!
We welcome past and current users of Dagger, and also those just interested in sitting in to learn more about Dagger.
RSE at MIT's JuliaLab, author of AMDGPU.jl, and maintainer of Dagger.jl. I want to improve the accessibility of HPC so that everyone can easily and productively scale their code to the max!
I am a postdoctoral researcher at the MIT JuliaLab and an HPC enthusiast who loves solving complex problems by thinking in parallel. My research intersects High-Performance Computing (HPC) and Artificial Intelligence (AI), exploring how advanced computational techniques can optimize AI algorithms for increased efficiency and effectiveness. I was honored as one of the Rising Stars in Computational and Data Sciences by U.S. Department of Energy. My collaborations extend internationally, including with the Innovative Computing Lab at the University of Tennessee and MINES ParisTech. In Summer 2021, I was a visiting scholar at the Innovative Computing Lab, where I contributed to a milestone of the Software for Linear Algebra Targeting Exascale (SLATE) project , a joint initiative of the U.S. Department of Energy’s Office of Science and the National Nuclear Security Administration (NNSA).
