Publication: A Parallel Hierarchical Approach for Automatic Differentiation
Introduction
Applications
Tools
Research Groups
Workshops
Publications
   List Publications
   Advanced Search
   Info
   Add Publications
My Account
About

A Parallel Hierarchical Approach for Automatic Differentiation

- incollection -
 

Author(s)
Marco Mancini

Published in
Automatic Differentiation of Algorithms: From Simulation to Optimization

Editor(s)
George Corliss, Christèle Faure, Andreas Griewank, Laurent Hascoët, Uwe Naumann

Year
2002

Publisher
Springer

Abstract
We evaluate in parallel first-order derivatives given a sequential computer program of the function to be differentiated. Our parallel implementation of an automatic differentiation (ad) algorithm is based on a hierarchical approach. The parallel method is developed by considering as a parallel computational model a shared-memory paradigm. The performance of the derivative codes is evaluated by considering a SGI Origin 2000 and by using the OPENMP standard library. In our computational experiments, we have considered the Flow in a Driven Cavity function belonging to the MINPACK-2 test problem collection. The computational results show the performance gain of the parallel approach over both the sequential one and the stripmining technique.

Cross-References
Corliss2002ADo

AD Theory and Techniques
Hierarchical Approach, Parallelism

BibTeX
@INCOLLECTION{
         Mancini2002APH,
       author = "Marco Mancini",
       title = "A Parallel Hierarchical Approach for Automatic Differentiation",
       pages = "231--236",
       chapter = "27",
       crossref = "Corliss2002ADo",
       booktitle = "Automatic Differentiation of Algorithms: From Simulation to Optimization",
       year = "2002",
       editor = "George Corliss and Christ{\`e}le Faure and Andreas Griewank and Laurent
         Hasco{\"e}t and Uwe Naumann",
       series = "Computer and Information Science",
       publisher = "Springer",
       address = "New York, NY",
       abstract = "We evaluate in parallel first-order derivatives given a sequential computer program
         of the function to be differentiated. Our parallel implementation of an automatic differentiation
         (AD) algorithm is based on a hierarchical approach. The parallel method is developed by considering
         as a parallel computational model a shared-memory paradigm. The performance of the derivative codes
         is evaluated by considering a SGI Origin 2000 and by using the OPENMP standard library. In our
         computational experiments, we have considered the {\em Flow in a Driven Cavity} function
         belonging to the MINPACK-2 test problem collection. The computational results show the performance
         gain of the parallel approach over both the sequential one and the stripmining technique.",
       ad_theotech = "Hierarchical Approach, Parallelism"
}


back
  

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