An object-oriented approach to development and testing of parallel solution algorithms for nonlinear PDES Page: 3 of 11
This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided to UNT Digital Library by the UNT Libraries Government Documents Department.
Extracted Text
The following text was automatically extracted from the image on this page using optical character recognition software:
An Object-Oriented Approach for Development and Testing
of Parallel Solution Algorithms for Nonlinear PDEs *
Richard D. Hornung t Carol S. Woodward t
Abstract
An object-oriented design that provides flexibility in simulation codes is presented.
This flexibility allows programmers freedom to easily change solution algorithms and
discretization schemes as well as add new solver packages as they become available.
Careful attention is paid to separating algorithm, data, and specific problem classes to
provide for ease in changing any of these components. Furthermore, data structures
are chosen so that each component works with data in a form best suited to its needs.
Lastly, we present some experiences and comments on the tradeoffs involved with this
design.
1 Introduction
We present an object-oriented software design for solving coupled systems of nonlinear,
time-dependent partial differential equations (PDEs). We concentrate on implicit time
solution methods for which there may be a variety of choices in components of the overall
solution algorithm. This design targets algorithmic flexibility and extensibility which
allow exploration of solution method alternatives. In particular, we separate solution
methods, data structures and discretization schemes. Our goal is to build a framework
that facilitates the exploration of algorithmic choices that arise when solving systems of
nonlinear PDEs. However, this paper focuses on software design issues that have arisen
during this development, especially those concerns related to the use of independently-
developed solver technology.
Flexibility among solution algorithm components is often an important ingredient in
the development of efficient, parallel simulation codes. In many cases, the "best" solution
approach is often a pursuit of ongoing research, especially for large-scale, three-dimensional
scientific applications. Codes may evolve substantially throughout their lifetimes forcing
significant changes to numerical solution algorithms and discretization methods. Reasons
for major changes may include advances in both linear and nonlinear solver technology, the
extension of modeling capabilities such as, new physics and chemistry "modules", or changes
to PDE discretizations. Consequently, software flexibility and extensibility may be critical
to useful application code development. Flexibility enables application code builders to
examine various algorithmic choices and to incorporate software packages developed apart
from the application project. Extensibility allows individual software components to be
This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore
National Laboratory under contract number W-7405-Eng-48.
tCenter for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA
94551, http://www.llnl.gov/CASC/, hornungl@llnl.gov, cswoodward@llnl.gov1
Upcoming Pages
Here’s what’s next.
Search Inside
This article can be searched. Note: Results may vary based on the legibility of text within the document.
Tools / Downloads
Get a copy of this page or view the extracted text.
Citing and Sharing
Basic information for referencing this web page. We also provide extended guidance on usage rights, references, copying or embedding.
Reference the current page of this Article.
Hornung, R & Woodward, C. An object-oriented approach to development and testing of parallel solution algorithms for nonlinear PDES, article, September 17, 1998; Livermore, California. (https://digital.library.unt.edu/ark:/67531/metadc785469/m1/3/: accessed April 25, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT Libraries Government Documents Department.