Optimised finite difference computation from symbolic equations
File(s)1707.03776v1.pdf (507.87 KB)
Accepted version
Author(s)
Type
Conference Paper
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.
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.
Date Issued
2017-07-16
Date Acceptance
2017-01-01
Citation
2017, pp.89-96
Start Page
89
End Page
96
Copyright Statement
Copyright○c 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
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
License URL
Identifier
http://arxiv.org/abs/1707.03776v1
Source
15th Python in Science Conference (SciPy 2017)
Subjects
cs.MS
cs.MS
Notes
Accepted for publication in Proceedings of the 16th Python in Science Conference (SciPy 2017)
Publication Status
Published
Start Date
2017-07-10
Finish Date
2017-07-16
Coverage Spatial
Austin, Texas, USA