NARAYANAN , Sri Hari Krishna

Development of a capable algorithmic differentiation (AD) tool requires large developer effort to provide the various flavors of derivatives, to experiment with the many AD model variants, and to apply them to the candidate application languages. Considering the relatively small size of the academic teams that develop AD tools, collaboration...

We apply an optimized method to the adjoint generation of a time-evolving land ice model through algorithmic differentiation (AD). The optimization involves a special treatment of the fixed-point iteration required to solve the nonlinear stress balance, which differs from a straightforward application of AD software, and leads to smaller memory...

Checkpointing is a cornerstone of data-flow reversal in adjoint algorithmic differentiation. Checkpointing is a storage/recomputation trade-off that can be applied at different levels, one of which being the call tree. We are looking for good placements of checkpoints onto the call tree of a given application, to reduce run time and memory...

International audience