Session

DescriptionPerformance portability and scalability on large-scale heterogeneous hardware represent crucial aspects challenging current scientific software development. Beyond software engineering considerations, workflows making further use of large datasets to constrain physical models are also emerging and are indispensable to develop, e.g., digital twins. GPU computing and differentiable programming constitute leading-edge tools that provide a promising way to combine physics-based simulations with novel machine learning and AI based methods to address interdisciplinary problems in science. The Julia language leverages both tools, as it includes first-class support for various accelerator types and an advanced compiler interface that supports native automatic differentiation capabilities. Julia makes it possible to differentiate efficiently through both CPU and GPU code without significant impact on performance. The goal of this minisymposium is to bring together scientists who work on or show interest in large-scale Julia HPC development, with a particular focus on the necessary tool stack for automatic differentiation and machine learning in the Julia GPU ecosystem, and on applications built on top of it. The selection of speakers, with expertise spanning from computer to domain science, offers a unique opportunity to learn about the latest development of Julia for HPC to drive discoveries in natural sciences.