Application of Algorithmic Differentiation for Exact Jacobians to the Universal Laminar Flame Solver

Hück, Alexander; Kreutzer, Sebastian; Messig, Danny; Scholtissek, Arne; Bischof, Christian; Hasse, Christian

doi:10.1007/978-3-319-93713-7_43

Cited by 2 publications

(1 citation statement)

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“…For more complex code bases, the change of the builtin floating-point type to a user-defined AD type can lead to several complications that have to be fixed by the AD expert. Complications typically arise due to (i) the different treatment of built-ins compared to user-defined types by the C++ language causing compilation errors [19], and (ii) the usage of external libraries [18], [20] and C-language function calls [21] that are not compatible with the AD type (as seen in LULESH). While tools exist to (partially) automate the process of the type change [22], these may not be able to handle, e.g., external solver libraries, which require special treatment in the adjoint context [23].…”

Section: A Ad-enhancement Of Luleshmentioning

confidence: 99%

Towards compiler-aided correctness checking of adjoint MPI applications

Hück

Protze

Lehr

et al. 2020

2020 IEEE/ACM 4th International Workshop on Software Correctness for HPC Applications (Correctness)

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Algorithmic Differentiation (AD) is a set of techniques to calculate derivatives of a computer program. In C++, AD typically requires (i) a type change of the built-in double, and (ii) a replacement of all MPI calls with AD-specific implementations. This poses challenges on MPI correctness tools, such as MUST, a dynamic checker, and TypeART, its memory sanitizer extension. In particular, AD impacts (i) memory layouts of the whole code, (ii) requires more memory allocations tracking by TypeART, and (iii) approximately doubles the MPI type checks of MUST due to an AD-specific communication reversal. To address these challenges, we propose a new callback interface for MUST to reduce the number of intercepted MPI calls, and, also, improve the filtering capabilities of TypeART to reduce tracking of temporary allocations for the derivative computation. We evaluate our approach on an AD-enhanced version of CORAL LULESH. In particular, we reduce stack variable tracking from 32 million to 13 thousand. MUST with TypeART and the callback interface reduces the runtime overhead to that of vanilla MUST.

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