Adjoint Code Design Patterns

Abstract

Adjoint methods have become fundamental ingredients of the scientific computing toolbox over the past decades. Large-scale parameter sensitivity analysis, uncertainty quantification, and nonlinear optimization would otherwise turn out computationally infeasible. The symbolic derivation of adjoint mathematical models for relevant problems in science and engineering and their implementation in consistency with the implementation of the underlying primal model frequently proves highly challenging. Hence, an increased interest in algorithmic adjoints can be observed. The algorithmic derivation of adjoint numerical simulation programs shifts some of the problems faced from functional and numerical analysis to computer science. It becomes a highly complex software engineering task requiring expertise in software analysis, transformation, and optimization. Despite rather mature software tool support for algorithmic differentiation, substantial user intervention is typically required when targeting nontrivial numerical programs. A large number of patterns shared by numerous application codes results in repeated duplication of development effort. The adjoint code design patterns introduced in this article aim to reduce this problem through improved formalization from the software engineering perspective. Fully functional reference implementations are provided through github.