Algorithmic differentiation (AD) is a set of techniques to obtain accurate derivatives of computer-implemented functions. For gradient-based numerical optimization purposes, the reverse mode of AD is especially suited — the run-time it needs to compute a gradient of the objective function is proportional to the run-time of the objective function, and independent of the number of design parameters.
In practice, classical AD tools require that the source code of the computer-implemented function is available, in a limited set of programming languages. As a step towards making AD applicable to cross-language or partially closed-source client programs, we developed the new AD tool Derivgrind . Derivgrind leverages the dynamic binary instrumentation framework Valgrind to add forward-mode AD logic to the machine code of compiled computer code.
In this talk, we present the new index-handling and tape-recording capabilities that we added to Derivgrind during the last months . In combination with a simple tape evaluator program, they enable operator-overloading-style reverse-mode AD for compiled programs.
 Max Aehle, Johannes Blühdorn, Max Sagebaum, Nicolas R. Gauger. Forward-Mode Automatic Differentiation of Compiled Programs. arXiv:2209.01895, 2022.
 Max Aehle, Johannes Blühdorn, Max Sagebaum, Nicolas R. Gauger. Reverse-Mode Automatic Differentiation of Compiled Programs. arXiv:2212.13760, 2022.
How to join online
You can join online via Zoom, using the following link:
Referent: Max Aehle, Chair for Scientific Computing (SciComp), University of Kaiserslautern-Landau (RPTU)
Zeit: 11:45 Uhr
Ort: Hybrid (Room 32-349 and via Zoom)