Computer Science, Machine Learning, and Applied Mathematics
TimeWednesday, June 2814:00 - 16:00 CEST
DescriptionNatural sciences and engineering applications increasingly leverage advanced computational methods to further improve our understanding of complex natural systems, using predictive modelling or analysing data. However, the flow of large amounts of data and the constant increase in spatiotemporal model resolution pose new challenges in scientific software development. Also, high-performance computing (HPC) resources massively rely on hardware accelerators such as graphics processing units (GPUs) that need to be efficiently utilised, representing an additional challenge. Performance portability and scalability as well as fast development on large-scale heterogeneous hardware represent crucial aspects in scientific software development that can be leveraged by the capabilities of the Julia language. The goal of this minisymposium is to bring together scientists who work on or show interest in large-scale Julia HPC development, including but not restricted to software ecosystems and portable programming models for development, GPU computing multiphysics solvers, and more. 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 Earth sciences featuring the next generation of 3D geophysical fluid dynamics solvers to leverage unprecedented resolution.