# Features of dolfin-adjoint¶

## Generality¶

dolfin-adjoint works for both steady and time-dependent models, and for both linear and nonlinear models. The user interface is exactly the same in both cases. For an example of adjoining a nonlinear time-dependent model, see the tutorial.

## Ease of use¶

dolfin-adjoint has been carefully designed to try to make its use as easy as possible. In many cases the only change to the forward model is to add

```
from dolfin_adjoint import *
```

at the top of the model. For example, deriving the adjoint of the tutorial example requires **adding
precisely three lines to the forward model**. dolfin-adjoint also makes it extremely easy to verify the correctness of the adjoint model.
It offers a powerful syntax for expressing general functionals.

## Efficiency¶

Efficiency of the resulting model is absolutely crucial for real applications. The efficiency of
an adjoint model is measured as (time for forward and adjoint run)/(time for forward run). Naumann (2011) states
that a typical value for this ratio when using algorithmic differentiation tools is in the range 3–30. By contrast, dolfin-adjoint is **extremely efficient**;
consider the following examples from the papers:

PDE | Theoretical optimum | Achieved efficiency |
---|---|---|

Cahn-Hilliard | 1.2 | 1.22 |

Stokes | 2.0 | 1.86 |

Viscoelasticity | 2.0 | 2.029 |

Gross-Pitaevskii | 1.5 | 1.54 |

Gray-Scott | 2.0 | 2.04 |

Navier-Stokes | 1.33 | 1.41 |

Mathematical programming with equilibrium constraints | 1.125 | 1.126 |

Shallow water | 1.125 | 1.125 |

Wetting and drying | 1.5 | 1.55 |

## Parallelism¶

Parallelism is ubiquitous in modern computational science. However,
applying algorithmic differentiation to parallel codes is still a
major research challenge. Algorithmic differentiation tools must be
specially modified to understand MPI and OpenMP directives, and
translate them into their parallel equivalents. By contrast, *because
of the high-level abstraction taken in libadjoint, the problem of
parallelism simply disappears*. In fact, there is no code whatsoever
in either dolfin-adjoint or pyadjoint to handle parallelism; by
deriving the adjoint at the right level of abstraction, the problem no
longer exists. **If the forward model runs in parallel, the adjoint
model also runs in parallel, with no modification.**

For more details, see the manual section on parallelism and the dolfin-adjoint paper.