Adjoint Algorithmic Differentiation (AAD) offers an efficient and robust alternative to compute sensitivities compared to the traditional bump-and-revalue approach (finite difference). However its implementation into large existing quant analytics libraries is often considered challenging.
Xcelerit provides consulting and advisory services to help clients deploy AAD solutions. Xcelerit experts have successfully delivered AAD projects for a number of tier 1 investment banks. Xcelerit team has developed a unique workflow to help clients AAD-enable large in-house quantitative finance analytics libraries (typically multi-millions lines of code), while respecting memory constraints, and achieving large performance gains.
In this paper we will zoom on Algorithmic Differentiation as an efficient and robust alternative to compute sensitivities. We cover the theory and then focus on practical examples. The paper further gives guidelines on how to cope with the memory requirements, handle parallelisation, and how to incorporate external library functions.
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