Abstract:Algorithmic Differentiation (AD) shown to be an essential tool to get sensitivity information in multiple areas of science such as Computational Fluid Dynamics (CFD) applications or finance. Yet there is no sufficient tool to ease the cost of providing performance portable AD codes, especially for modern hardware like GPU clusters. This paper sketches our plans and progress so far to extend the OPS framework with an adjoint tape (storage for descriptors of intermediate steps and intermediate states of variable… Show more
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