Optimised finite difference computation from symbolic equations

07/12/2017
by   Michael Lange, et al.
0

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.

READ FULL TEXT

page 3

page 4

page 6

research
08/30/2016

Devito: automated fast finite difference computation

Domain specific languages have successfully been used in a variety of fi...
research
09/12/2016

Devito: Towards a generic Finite Difference DSL using Symbolic Python

Domain specific languages (DSL) have been used in a variety of fields to...
research
08/06/2018

Devito: an embedded domain-specific language for finite differences and geophysical exploration

We introduce Devito, a new domain-specific language for implementing hig...
research
02/13/2022

Faster Gröbner Bases via Domain-Specific Ordering in Parameter Identifiability of ODE Models

We consider a specific class of polynomial systems that arise in paramet...
research
10/20/2020

Temporal blocking of finite-difference stencil operators with sparse "off-the-grid" sources

Stencil kernels dominate a range of scientific applications, including s...
research
07/09/2018

Architecture and performance of Devito, a system for automated stencil computation

Stencil computations are a key part of many high-performance computing a...

Please sign up or login with your details

Forgot password? Click here to reset