Parallel derivative computation using ADOL-C

Abstract

Derivative computation using Automatic Differentiation (AD) is often considered to operate purely serial. Performing the differentiation task in parallel may require the applied AD-tool to extract parallelization information from the user function, transform it, and apply this new strategy in the differentiation process. Furthermore, when using the reverse mode of AD, it must be ensured that no data races are introduced due to the reversed data access scheme. Considering an operator overloading based AD-tool, an additional challenge is to be met: Parallelization statements are typically not recognized. In this paper, we present and discuss the parallelization approach that we have integrated into ADOL-C, an operator overloading based AD-tool for the differentiation of C/C++ programs. The advantages of the approach are clarified by means of the parallel differentiation of a function that handles the time evolution of a 1D-quantum plasma.