High-Performance Derivative Computations using CoDiPack

TL;DR

CoDiPack is a new AD tool optimized for large-scale software, offering minimal memory use and fast runtime through modular design and recursive data structures, supporting various taping approaches.

Contribution

It introduces CoDiPack, a flexible, efficient AD tool with a modular architecture suitable for large-scale applications and multiple taping strategies.

Findings

Demonstrates high performance on PDE example

Achieves efficient memory usage and runtime

Supports multiple taping approaches

Abstract

There are several AD tools available, which all implement different strategies for the reverse mode of AD. The major strategies are primal value taping (implemented e.g. by ADOL-c) and Jacobi taping (implemented e.g. by adept and dco/c++). Especially for Jacobi taping, recent advances by using expression templates make this approach very attractive for large scale software. The current implementations are either closed source or miss essential features and flexibility. Therefore, we present the new AD tool CoDiPack (Code Differentiation Package) in this paper. It is specifically designed for a minimal memory consumption and optimal runtime, such that it can be used for the differentiation of large scale software. An essential part of the design of CoDiPack is the modular layout and the recursive data structures, which do not only allow the efficient implementation of the Jacobi taping approach, but will also enable other approaches like the primal value taping or new research ideas. We will also present the performance value of CoDiPack on a generic PDE example and on the SU2 code.