Michael Lange
Imperial College London

Navjot Kukreja
Imperial College London

Fabio Luporini
Imperial College London

Mathias Louboutin
The University of British Columbia

Charles Yount
Intel Corporation

Jan Hückelheim
Imperial College London

Gerard J. Gorman
Imperial College London

Video: https://youtu.be/KinmqFTEs94

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.

Keywords

Finite difference, domain-specific languages, symbolic Python

DOI

10.25080/shinma-7f4c6e7-00d

Bibtex entry

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Copyright © 2017 Michael Lange et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.