Hybrid static/dynamic activity analysis

Authors:
Barbara Kreaseck;Luis Ramos;Scott Easterday;Michelle Strout;Paul Hovland
Affiliations:
La Sierra University, Riverside, CA;La Sierra University, Riverside, CA;La Sierra University, Riverside, CA;Colorado State University, Fort Collins;Argonne National Laboratory
Venue:
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Year:
2006

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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.