Optimised finite difference computation from symbolic equations

TL;DR

This paper introduces Devito, a high-productivity, open-source framework that automatically generates optimized finite difference code from symbolic equations in Python, facilitating scientific computing and seismic inversion tasks.

Contribution

The paper presents Devito, a novel framework that automates the creation of efficient finite difference code from symbolic PDE definitions, enhancing productivity and performance in scientific computing.

Findings

Devito generates highly optimized stencil code from simple symbolic Python scripts.

The framework effectively applies to seismic inversion problems.

Automated code generation improves computational efficiency and developer productivity.

Abstract

Domain-specific high-productivity environments are playing an increasingly important role in scientific computing due to the levels of abstraction and automation they provide. In this paper we introduce Devito, an open-source domain-specific framework for solving partial differential equations from symbolic problem definitions by the finite difference method. We highlight the generation and automated execution of highly optimized stencil code from only a few lines of high-level symbolic Python for a set of scientific equations, before exploring the use of Devito operators in seismic inversion problems.