ENABLING USER-DRIVEN CHECKPOINTING STRATEGIES IN REVERSE-MODE AUTOMATIC DIFFERENTIATION

Abstract. This paper presents a new functionality of the Automatic Dierentiation (AD) Tool tapenade. tapenade generates adjoint codes which are widely used for optimization or inverse problems. 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 problem, tapenade implements a sub-optimal version of a technique called checkpointing, which is a trade-o between storage and recomputation. Our long-term goal is to provide an optimal checkpointing strategy for every code, not yet achieved by any AD tool. Towards that goal, we rst introduce modications in tapenade in order to give the user the choice to select the checkpointing strategy most suitable for their code. Second, we conduct experiments in real-size scientic codes in order to gather hints that help us to deduce an optimal checkpointing strategy. Some of the experimental results show memory savings up to 35 % and execution time up to 90%.