Accelerating Geocomputing with Julia at Scale

The ongoing digitalisation in geosciences, along with higher resolution data and more powerful computers, necessitates efficient workflows and tools for processing large amounts of data, modelling processes, and addressing new problems and challenges.

The Julia language offers an great basis for this by combining the benefits of a high-level language, such as ease of use and interactivity, with the features of a low-level language, including speed, efficiency, scalability, and native GPU support.

We will present recent efforts to accelerate geocomputing using Julia at scale. These efforts are supported by the PASC-funded GPU4GEO project, in collaboration with the Swiss National Supercomputing Centre. We will showcase the current HPC building blocks that we have developed, which enables geoscientists to write high-performance stencil codes that can scale from their laptops to the largest supercomputers. Additionally, we will demonstrate preliminary results on using automatic differentiation to perform inverse modelling. This allows us to efficiently constrain models with data.

Additionally, we discuss how these recent developments, along with future ones, will accelerate geocomputing and enable the education of the next generation of geoscientists.