Hybrid static/dynamic activity analysis

Barbara Kreaseck, Luis Ramos, Scott Easterday, Michelle Strout, Paul Hovland

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationComputational Science - ICCS 2006
Subtitle of host publication6th International Conference, Proceedings
Number of pages9
ISBN (Print)3540343857, 9783540343851
StatePublished - 2006
Externally publishedYes
EventICCS 2006: 6th International Conference on Computational Science - Reading, United Kingdom
Duration: May 28 2006May 31 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3994 LNCS - IV
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceICCS 2006: 6th International Conference on Computational Science
Country/TerritoryUnited Kingdom

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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