High-level python abstractions for optimal checkpointing in inversion problems

Inversion and PDE-constrained optimization problems often rely on solving the adjoint problem to calculate the gradient of the objective function. This requires storing large amounts of intermediate data, setting a limit to the largest problem that might be solved with a given amount of memory available. Checkpointing is an approach that can reduce the amount of memory required by redoing parts of the computation instead of storing intermediate results. The Revolve checkpointing algorithm offers an optimal schedule that trades computational cost for smaller memory footprints. Integrating Revolve into a modern python HPC code and combining it with code generation is not straightforward. We present an API that makes checkpointing accessible from a DSL-based code generation environment along with some initial performance figures with a focus on seismic applications.