Accelerating innovation with software abstractions for scalable computational geophysics

TL;DR

The paper introduces SLIM, an open-source software framework that enables scalable and flexible computational geophysics research by providing layered abstractions for high-performance computing and diverse applications.

Contribution

The paper presents a novel layered abstraction framework for computational geophysics that simplifies problem formulation and integrates advanced HPC techniques and external tools.

Findings

Demonstrated scalability on seismic inversion tasks

Enabled integration of physics-informed machine learning methods

Facilitated uncertainty quantification in geophysical models

Abstract

We present the SLIM (https://github.com/slimgroup) open-source software framework for computational geophysics, and more generally, inverse problems based on the wave-equation (e.g., medical ultrasound). We developed a software environment aimed at scalable research and development by designing multiple layers of abstractions. This environment allows the researchers to easily formulate their problem in an abstract fashion, while still being able to exploit the latest developments in high-performance computing. We illustrate and demonstrate the benefits of our software design on many geophysical applications, including seismic inversion and physics-informed machine learning for geophysics (e.g., loop unrolled imaging, uncertainty quantification), all while facilitating the integration of external software.