Parallel

Applying algorithmic differentiation tools to parallel source code is still a major research area, and most adjoint codes that work in parallel manually adjoin the parallel communication sections of their code.

One of the major advantages of the new high-level abstraction used in dolfin-adjoint is that the problem of parallelism in adjoint codes simply disappears: indeed, there is not a single line of parallel-specific code in dolfin-adjoint or pyadjoint. For more details on how this works, see the papers.

Therefore, if your forward model runs in parallel, your adjoint will also, with no modification. For example, let us take the adjoint verification program from the section on verification:

$ mpiexec -n 4 python tutorial4.py
...
Computed residuals: [8.7896393841476643e-07, 2.2008124728377051e-07, 5.5062931909424556e-08, 1.3771065246938211e-08]
Computed residuals: [8.7896393841476643e-07, 2.2008124728377051e-07, 5.5062931909424556e-08, 1.3771065246938211e-08]
Computed residuals: [8.7896393841476643e-07, 2.2008124728377051e-07, 5.5062931909424556e-08, 1.3771065246938211e-08]
Computed residuals: [8.7896393841476643e-07, 2.2008124728377051e-07, 5.5062931909424556e-08, 1.3771065246938211e-08]
Computed convergence rates: [1.9977677554998587, 1.9988828855094791, 1.9994412684498588]
Computed convergence rates: [1.9977677554998587, 1.9988828855094791, 1.9994412684498588]
Computed convergence rates: [1.9977677554998587, 1.9988828855094791, 1.9994412684498588]
Computed convergence rates: [1.9977677554998587, 1.9988828855094791, 1.9994412684498588]

In the next section we discuss how to express functionals with different time dependencies.