**Devito**: Symbolic Finite Difference Computation

Devito is a Domain-specific Language (DSL) and code generation framework for the design of highly optimised finite difference kernels for use in inversion methods. Devito utilises SymPy to allow the definition of operators from high-level symbolic equations and generates optimised and automatically tuned code specific to a given target architecture.

Symbolic computation is a powerful tool that allows users to:

- Build complex solvers from only a few lines of high-level code
- Use automated performance optimisation for generated code
- Adjust stencil discretisation at runtime as required
- (Re-)development of solver code in hours rather than months

**Documentation**

Documentation for Devito is available here, including installation instructions, a set of tutorials and API documentation. In addition, a paper outlining the use of symbolic Python to define finite difference operators in Devito can be found here. Devito is a fast moving project so some of the documentation may lag behind development. Feel free to talk to us on slack if you have questions - PR’s are also welcome.

```
from devito import *
grid = Grid(shape=(nx, ny))
u = TimeFunction(name='u', grid=grid,
space_order=2)
u.data[0, :] = initial_data[:]
eqn = Eq(u.dt, a * (u.dx2 + u.dy2))
stencil = solve(eqn, u.forward)
op = Operator(Eq(u.forward, stencil))
op(t=timesteps, dt=dt)
```

Example code for a 2D diffusion operator from a symbolic definition. The full tutorial can be found here. In order to get more faimliar with Devito, it is highly recommended that you run more tutorials .

### Seismic Inversion using Devito

Devito is primarily designed to create wave propagation kernels for
use in seismic inversion problems. A tutorial for the generation of a
modelling operator using an acoustic wave equation can be found
here and a paper
outlining the verification procedures of the acoustic operator can be
found here.

### Optimisation and Performance

Devito provides a set of automated performance optimizations during code generation that allow user applications to fully utilise the target hardware without changing the model specification:

- Vectorisation and shared-memory parallelism
- Loop blocking and auto-tuning
- Symbolic optimisations:
- Common sub-expression elimination (CSE)
- Loop re-writing and expression hoisting

Performance of acoustic wave modelling operator with different stencil sizes and auto-tuning on single-socket E5-2697 v4 CPU (Broadwell, 16 cores @ 2.3GHz).

### Citing Devito

If you publish results using Devito, we would be grateful if you would cite the following papers, submitted to ACM Transactions on Mathematical Software (TOMS) and Geoscientific Model Development (GMD):

```
@article{devito-compiler,
author = { {Luporini}, F. and {Lange}, M. and {Louboutin}, M. and {Kukreja}, N. and {H{\"u}ckelheim}, J. and {Yount}, C. and {Witte}, P. and {Kelly}, P.~H.~J. and {Gorman}, G.~J. and {Herrmann}, F.~J. },
title = { Architecture and performance of Devito, a system for automated stencil computation },
journal = { CoRR },
volume = { abs/1807.03032 },
month = { jul },
year = { 2018 },
url = { http://arxiv.org/abs/1807.03032 },
archivePrefix = { arXiv },
eprint = { 1807.03032 }
}
@article{devito-api,
author = { {Louboutin}, M. and {Lange}, M. and {Luporini}, F. and {Kukreja}, N. and {Witte}, P.~A. and {Herrmann}, F.~J.
and {Velesko}, P. and {Gorman}, G.~J. },
title = {Devito: an embedded domain-specific language for finite differences and geophysical exploration},
journal = { CoRR },
volume = { abs/1808.01995 },
month = { Aug },
year = { 2018 },
url = { https://arxiv.org/abs/1808.01995 },
archivePrefix = { arXiv },
eprint = { 1808.01995 }
}
```