Event-Based Automatic Differentiation of OpenMP with OpDiLib

TL;DR

OpDiLib is a novel software tool that enables automatic differentiation of OpenMP parallelized code using operator overloading, supporting modern features and achieving high performance without source code modifications.

Contribution

It introduces an event-based approach for AD of OpenMP code with operator overloading, extending support to modern OpenMP features and enabling high-performance differentiation.

Findings

Supports differentiation without source code modifications

Achieves significant speedup and scalability in experiments

Handles modern OpenMP features with minimal restrictions

Abstract

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. With it, we establish support for OpenMP features in a reverse mode operator overloading AD tool to an extent that was previously only reported on in source transformation tools. We achieve this with an event-based implementation ansatz that is unprecedented in AD. Combined with modern OpenMP features around OMPT, we demonstrate how it can be used to achieve differentiation without any additional modifications of the source code; neither do we impose a priori restrictions on the data access patterns, which makes OpDiLib highly applicable. For further performance optimizations, restrictions like atomic updates on adjoint variables can be lifted in a fine-grained manner. OpDiLib can also be applied in a semi-automatic fashion via a macro interface, which supports compilers that do not implement OMPT. We demonstrate the applicability of OpDiLib for a pure operator overloading approach in a hybrid parallel environment. We quantify the cost of atomic updates on adjoint variables and showcase the speedup and scaling that can be achieved with the different configurations of OpDiLib in both the forward and the reverse pass.