Abstract
In forward mode Automatic Differentiation, the derivative program computes a function f and its derivatives, f′. Activity analysis is important for AD. Our results show that when all variables are active, the runtime checks required for dynamic activity analysis incur a significant overhead. However, when as few as half of the input variables are inactive, dynamic activity analysis enables an average speedup of 28% on a set of benchmark problems. We investigate static activity analysis combined with dynamic activity analysis as a technique for reducing the overhead of dynamic activity analysis. © Springer-Verlag Berlin Heidelberg 2006.
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CITATION STYLE
Kreaseck, B., Ramos, L., Easterday, S., Strout, M., & Hovland, P. (2006). Hybrid static/dynamic activity analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3994 LNCS-IV, pp. 582–590). Springer Verlag. https://doi.org/10.1007/11758549_80
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