Argonne National Laboratory

AD-Suite - A Test Suite for Algorithmic Differentiation

TitleAD-Suite - A Test Suite for Algorithmic Differentiation
Publication TypeReport
Year of Publication2016
AuthorsNarayanamurthi, M, Bosse, T, Narayanan, SHK, Hovland, PD

Algorithmic differentiation (AD) is a widely accepted methodology to obtain derivatives of scientific code for use in optimization, integration methods, and sensitivity analysis. Currently about 60 AD tools are listed on autodiff. org to compute sensitivity information. Several years have been invested in the research and development of popular implementations such as ADOL-C, CppAD, OpenAD, and Tapenade. In most cases, however, the test code that comes packaged along with the tools comprises either toy or academic examples that are far removed from real-life applications. In general testing scientific software is difficult. A common reason is the lack of separation between theory and code in a scientist’s mind that limits testing to a mere verification of theory [7].