JuliaCon 2023

Ivan Utkin

Ivan Utkin's scientific record revolves around numerical modelling of natural processes in geosciences with a strong emphasis on resolving coupled multi-physics interactions. Specifically, the record includes studies of fluid flows in porous rocks, including compaction-driven flow focussing, ground displacement due to the elastic response of rocks to the fluid pressure, and the influence of chemical interactions between fluid and rocks on the flow dynamics and observed chemical composition in rocks. A particular focus of Utkin's research is the development of numerical techniques to resolve processes in high resolution using massively parallel computing architectures such as graphics processing units (GPUs).


Sessions

07-25
09:00
180min
Differentiable modelling on GPUs
Ludovic Räss, Samuel Omlin, Ivan Utkin

Why to wait hours for computations to complete, when it could take only a few seconds? Tired of prototyping code in an interactive, high-level language and rewriting it in a lower-level language to get high-performance code? Unsure about the applicability of differentiable programming? Or simply curious about parallel and GPU computing and automatic differentiation being game changers in physics-based and data-driven modelling.

HPC
32-123
07-28
11:30
30min
Massively parallel inverse modelling on GPUs with Enzyme
Ludovic Räss, Samuel Omlin, Ivan Utkin

We present an efficient and scalable approach to inverse PDE-based modelling with the adjoint method. We use automatic differentiation (AD) with Enzyme to automaticaly generate the buidling blocks for the inverse solver. We utilize the efficient pseudo-transient iterative method to achieve performance that is
close to the hardware limit for both forward and adjont problems. We demonstrate close to optimal parallel efficiency on GPUs in series of benchmarks.

JuliaCon
26-100
07-28
14:00
30min
Scalable 3-D PDE Solvers Tackling Hardware Limit
Ludovic Räss, Samuel Omlin, Ivan Utkin

We present an efficient approach for the development of 3-D partial differential equation solvers that are able to tackle the hardware limit of modern GPUs and scale to the world's largest supercomputers. The approach relies on the accelerated pseudo-transient method and on the automatic generation of on-chip memory usage-optimized computation kernels for the implementation. We report performance and scaling results on LUMI and Piz Daint, an AMD-GPU and a NVIDIA-GPU supercomputer.

HPC
32-124