Publication: Design and Implementation of a Context-Sensitive, Flow-Sensitive Activity Analysis Algorithm for Automatic Differentiation
Introduction
Applications
Tools
Research Groups
Workshops
Publications
   List Publications
   Advanced Search
   Info
   Add Publications
My Account
About

Design and Implementation of a Context-Sensitive, Flow-Sensitive Activity Analysis Algorithm for Automatic Differentiation

- incollection -
 

Author(s)
Jaewook Shin , Priyadarshini Malusare , Paul D. Hovland

Published in
Advances in Automatic Differentiation

Editor(s)
Christian H. Bischof, H. Martin Bücker, Paul D. Hovland, Uwe Naumann, J. Utke

Year
2008

Publisher
Springer

Abstract
Automatic differentiation (ad) has been expanding its role in scientific computing. While several ad tools have been actively developed and used, a wide range of problems remain to be solved. Activity analysis allows ad tools to generate derivative code for fewer variables, leading to a faster run time of the output code. This paper describes a new contextsensitive, flow-sensitive (CSFS) activity analysis, which is developed by extending an existing context-sensitive, flow-insensitive (CSFI) activity analysis. Our experiments with eight benchmarks show that the new CSFS activity analysis is more than 27 times slower but reduces 8 overestimations for the MIT General Circulation Model (MITgcm) and 1 for an ODE solver (c2) compared with the existing CSFI activity analysis implementation. Although the number of reduced overestimations looks small, the additionally identified passive variables may significantly reduce tedious human effort in maintaining a large code base such as MITgcm.

Cross-References
Bischof2008AiA

AD Theory and Techniques
Data Flow Analysis

BibTeX
@INCOLLECTION{
         Shin2008DaI,
       title = "Design and Implementation of a Context-Sensitive, Flow-Sensitive Activity Analysis
         Algorithm for Automatic Differentiation",
       doi = "10.1007/978-3-540-68942-3_11",
       author = "Jaewook Shin and Priyadarshini Malusare and Paul D. Hovland",
       abstract = "Automatic differentiation (AD) has been expanding its role in scientific computing.
         While several AD tools have been actively developed and used, a wide range of problems remain to be
         solved. Activity analysis allows AD tools to generate derivative code for fewer variables, leading
         to a faster run time of the output code. This paper describes a new contextsensitive, flow-sensitive
         (CSFS) activity analysis, which is developed by extending an existing context-sensitive,
         flow-insensitive (CSFI) activity analysis. Our experiments with eight benchmarks show that the new
         CSFS activity analysis is more than 27 times slower but reduces 8 overestimations for the MIT
         General Circulation Model (MITgcm) and 1 for an ODE solver (c2) compared with the existing CSFI
         activity analysis implementation. Although the number of reduced overestimations looks small, the
         additionally identified passive variables may significantly reduce tedious human effort in
         maintaining a large code base such as MITgcm.",
       crossref = "Bischof2008AiA",
       pages = "115--125",
       booktitle = "Advances in Automatic Differentiation",
       publisher = "Springer",
       editor = "Christian H. Bischof and H. Martin B{\"u}cker and Paul D. Hovland and Uwe
         Naumann and J. Utke",
       isbn = "978-3-540-68935-5",
       issn = "1439-7358",
       year = "2008",
       ad_theotech = "Data Flow Analysis"
}


back
  

Contact:
autodiff.org
Username:
Password:
(lost password)