Enabling User-Driven Checkpointing Strategies in Reverse-Mode Automatic Differentiation

This paper presents a new functionality of the Automatic Differentiation (AD) Tool Tapenade. Tapenade generates adjoint codes which are widely used for optimization in CFD. Unfortunately, for large applications the adjoint code demands a great deal of memory, because it needs to store a large set of intermediates values. To cope with that...