13 citations · 23 across the 6 of their papers we have counts for
4 papers · 1 filter
Reverse-Mode Automatic Differentiation of Compiled Programs
Max Aehle, Johannes Blühdorn, Max Sagebaum +1
Tools for algorithmic differentiation (AD) provide accurate derivatives of computer-implemented functions for use in, e. g., optimization and machine learning (ML). However, they o…
Forward-Mode Automatic Differentiation of Compiled Programs
Max Aehle, Johannes Blühdorn, Max Sagebaum +1
Algorithmic differentiation (AD) is a set of techniques that provide partial derivatives of computer-implemented functions. Such a function can be supplied to state-of-the-art AD t…
Event-Based Automatic Differentiation of OpenMP with OpDiLib
Johannes Blühdorn, Max Sagebaum, Nicolas R. Gauger
We present the new software OpDiLib, a universal add-on for classical operator overloading AD tools that enables the automatic differentiation (AD) of OpenMP parallelized code. Wit…
Assign optimization for algorithmic differentiation reuse index management strategies
Max Sagebaum, Johannes Blühdorn, Nicolas R. Gauger
The identification of primal variables and adjoint variables is usually done via indices in operator overloading algorithmic differentiation tools. One approach is a linear managem…