Table Of ContentTHE ARCHITECTURE OF
SCIENTIFIC SOFlWARE
IFIP -The International Federation for Information Processing
IFIP was founded in 1960 under the auspices of UNESCO, following the First World
Computer Congress held in Paris the previous year. An umbrella organization for societies
working in information processing, IFIP's aim is two-fold: to support information
processing within its member countries and to encourage technology transfer to developing
nations. As its mission statement clearly states,
IFIP's mission is to be the leading, truly international, apolitical organization which
encourages and assists in the development, exploitation and application of information
technology for the benefit of all people.
IFIP is a non-profitmaking organization, run almost solely by 2500 volunteers. It operates
through a number of technical committees, which organize events and publications. IFIP's
events range from an international congress to local seminars, but the most important are:
• The IFIP World Computer Congress, held every second year;
• open conferences;
• working conferences.
The flagship event is the IFIP World Computer Congress, at which both invited and
contributed papers are presented. Contributed papers are rigorously refereed and the
rejection rate is high.
As with the Congress, participation in the open conferences is open to all and papers may
be invited or submitted. Again, submitted papers are stringently refereed.
The working conferences are structured differently. They are usually run by a working
group and attendance is small and by invitation only. Their purpose is to create an
atmosphere conducive to innovation and development. Refereeing is less rigorous and
papers are subjected to extensive group discussion.
Publications arising from IFIP events vary. The papers presented at the IFIP World
Computer Congress and at open conferences are published as conference proceedings, while
the results of the working conferences are often published as collections of selected and
edited papers.
Any national society whose primary activity is in information may apply to become a full
member ofIFIP, although full membership is restricted to one society per country. Full
members are entitled to vote at the annual General Assembly, National societies preferring
a less committed involvement may apply for associate or corresponding membership.
Associate members enjoy the same benefits as full members, but without voting rights.
Corresponding members are not represented in IFIP bodies. Affiliated membership is open
to non-national societies, and individual and honorary membership schemes are also offered.
THE ARCHITECTURE OF
SCIENTIFIC SOFTWARE
IFlP TC2jWG2.5 Working Conference on the
Architecture of Scientific Software
October 2-4, 2000, Ottawa, Canada
Edited by
Ronald F. Boisvert
National Institute of Standards and Technology
USA
Ping Tak Peter Tang
Intel Corporation
USA
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SPRINGER SCIENCE+BUSINESS MEDIA, LLC
Library of Congress Cataloging-in-Publication Data
IFIP TC2/WG2.5 Working Conference on the Architecture ofScientific Software (2000:
Ottawa, Canada)
The architecture of scientific software: IFIP TC2/WG2.5 Working Conference on the
Architecture of Scientific Software, October 2-4, 2000, Ottawa, Canada / edited by
Ronald F. Boisvert, Ping Tak Peter Tang.
Inc1udes bibliographical references and index.
ISBN 978-1-4757-6719-3 ISBN 978-0-387-35407-1 (eBook)
DOI 10.1007/978-0-387-35407-1
1. Software engineering-Congresses. 2. Computer architecture-Congresses. 3.
Science-Computer software-Congresses. 1. Boisvert, Ronald F. II. Tang, Pink Tak
Peter. III. Title.
QA76.758 .l3452000
005.l-dc21 2001020365
Copyright © 2001 by Springer Science+Business Media New York
Originally published by Kluwer Academic Publishers in 2001
Ali rights reserved. No part of this publication may be reproduced, stored in a retrieval
system or transmitted in any form or by any means, mechanical, photo-copying, recording,
or otherwise, without the prior written permission of the publisher,
Springer Science+Business Media, LLC
Printed on acid-free paper.
Contents
Preface ix
Contributing Authors xv
Part I Large-Scale Systems Integration
Network-Based Scientific Computing 3
Elias N. Houstis, Ann Christine Catlin, Ganesh Balakrishnan,
Nitesh Dhanjani, GaHyun Park, John R. Rice, Spyros Lalis,
Manolis Stamatogiannakis, Catherine Houstis
Future Generations of Problem-Solving Environments 29
Jose C. Cunha
Developing an Architecture to Support the Implementation and 39
Development of Scientific Computing Applications
Dorian C. Arnold, Jack J. Dongarra
PETSc and Overture: Lessons Learned Developing an Interface 57
between Components
Kristopher R. Buschelman, William D. Gropp, Lois C. McInnes,
Barry F. Smith
Component Technology for High-Performance Scientific 69
Simulation Software
Tom Epperly, Scott Kohn, Gary Kum/ert
A New Approach to Software Integration Frameworks for 87
Multi-physics Simulation Codes
Eric de Sturler, Jay Hoe/linger, Laxmikant Kale, Milind Bhandarkar
Code Coupling using Parallel CORBA Objects 105
Christophe Rene, Thierry Priol, Guillaume AlMon
VI ARCHITECTURE OF SCIENTIFIC SOFTWARE
A Collaborative Code Development Environment for 119
Computational Electro-magnetics
Matthew Shields, Omer F. Rana, David. W. Walker, David Golby
Part II The Architecture of Components
On the Role of Mathematical Abstractions for Scientific Computing 145
Krister Ahlander, Magne Haveraaen, Hans Z. Munthe-Kaas
Object-oriented Modeling of Parallel PDE Solvers 159
Michael Thune, Krister Ahlander, Malin Ljungberg, Markus Norden,
Kurt Otto, Jarmo Rantakokko
Broadway: A Software Architecture for Scientific Computing 175
Samuel Z. Guyer, Calvin Lin
Formal Methods for High-Performance Linear Algebra Libraries 193
John A. Gunnels, Robert A. van de Geijn
New Generalized Matrix Data Structures Lead to a Variety of 211
High-Performance Algorithms
Fred G. Gustavson
A Comprehensive DFT API for Scientific Computing 235
Ping Tak Peter Tang
Using A Fortran Interface to POSIX Threads 257
Richard J. Hanson, Clay P. Breshears and Henry A. Gabb
Data Management Systems for Scientific Applications 273
Reagan W. Moore
Software Components for Application Development 285
Arnaud Desitter, Antoine Le Hyaric, Geoff Morgan, Gareth Shaw,
Anne Trefethen
Hierarchichal Representation and Computation of Approximate 301
Solutions in Scientific Simulations
Wayne H. Enright
Software Architecture for the Investigation of Controllable Models 317
with Complex Data Sets
Dmitry Belyshev and Vladimir Gurman
A Mixed-Language Programming Methodology for High 333
Performance Java Computing
Vladimir S. Getov
Contents vii
Part III Conference Information
The Architecture of Scientific Software: the Conference 351
Index 357
Preface
IFIP Working Group 2.5 on Numerical Software has always been con
cerned not just with strictly numerical issues, such as details of floating
point arithmetic or numerical algorithms, but also with the software is
sues that affect the viability of scientific computation. Previous working
conferences sponsored by WG2.5 have included focus on software porta
bility, programming languages for numerical software, programming and
problem solving environments, parallelism, performance evaluation and
software quality. These issues are, of course, of wider significance be
yond numerical software. The solid basis of a practical perspective on
scientific computation has lead the work of WG2.5 to be pioneering even
in this wider context.
This working conference addresses another such topic, one only re
cently recognized as a critical aspect of software design, but a topic
increasingly important as software gets larger and more complex, con
structed by teams of people and evolved over decades. This topic is
software architecture. Software architecture refers to the way software
is structured to promote objectives such as reusability, maintainability,
extensibility, feasibility of independent implementation, etc. In the con
text of scientific computation, the challenge facing mathematical soft
ware practitioners is to design, develop and supply computational com
ponents which deliver these objectives when embedded in end-user ap
plication codes.
At one time, scientific computing applications were sufficiently sim
ple that it was feasible with each new computational problem for one to
start from scratch and construct a monolithic program specific to solving
that particular problem. As these applications became more complex,
it became necessary to amortize the development effort over many sim
ilar computational problems. More significantly, it became necessary to
be able to take advantage of rare, highly specialized, skills through em
ploying off-the-shelf software components implemented by third parties.
Thus for many years, the software architecture of a scientific computing
application has typically been that the computation is effected not by
x ARCHITECTURE OF SCIENTIFIC SOFTWARE
just a single program but by the operation of a suite of related programs
acting on a common database. Within such a suite, the individual pro
grams are structured from subprograms. Some of these subprograms are
obtained from libraries provided by various commercial suppliers, and
some from public domain sources. A few subprograms will be unique
to this suite of programs, representing the particular modeled science
in the suite and the desired sequences of operations to be performed.
Commonly, the user provides the main program that calls the subpro
grams. In some cases, however, the main program is a framework that
can realize different computations by making appropriate calls to the
available subprograms, including any user-provided plug-in, and control
over the sequence of calls and their parameters is provided by the user
through a problem oriented scripting language.
Today, new options for architectures for scientific computation are
becoming available, and some of the older paradigms may need to be
re-thought. What are the implications of widespread connectivity to
networks, in terms of the construction of scientific computing applica
tions in the future? Do distributed computing models such as CORBA
and ActiveX, or the RMI (Remote Method Invocation) of Java, form
an appropriate basis on which to build higher level protocols (e.g., for
interacting mathematical and geometric objects) in pursuit of the goal
of "plug-and-play" application inter-operability? Do we need to extend
the notion of a common database to embrace federated databases, which
may be geographically or organizationally dispersed across the Web, and
only intermittently available? How can we exploit concurrency and par
allel execution for monitoring, visualization and steering purposes, for
instance, as well as for straightforward performance? If, as many people
argue, object-oriented computing provides a more appropriate program
ming basis than procedural languages, how can the properties (such as
reliability, portability, efficiency, etc.) so painstakingly pursued by the
developers of "traditional" subroutine libraries be preserved?
With many conferences being held on the topic of software archi
tecture, what is the added value of holding a conference on software
architecture for scientific computing applications? Why might we antic
ipate software architecture for scientific applications might differ from
software architecture suitable for other situations? What are distinctive
characteristics of scientific applications?
Scientific applications often are very large computations that strain
the resources of whatever computers are available. Clever algorithms
and data structures may need to be utilized so that the computation
can be done with acceptable efficiency, or even so that it can be done
at all. The intended computation is often ill defined, or at least incom-
PREFACE Xl
pletely defined, and completion of the specification must be suggested
by experience, by analogy to other computations, or by heuristics. Data
types may include not just structures of text and numbers, but the full
range of multimedia, from sound to simultaneous time series, from still
images to video. Scientific applications typically require deep scientific
and domain knowledge, and depend on subtle interplay of different ap
proximations. They often implement sophisticated mathematics. In
many cases, numerical computations need to be carefully arranged not
just to avoid inaccurate results, but to avoid instability of processes.
Sensitivity to external input data can be a problem, and inadequate in
put data is common. Insightful display of computed quantities may be
essential for analyzing and interpreting results or for interactive control
to steer the computation. Scientific computations are often experiments,
and must be controlled, recorded, and categorized so that they can be
compared to other experimental observations.
These characteristics make the implementation of scientific applica
tions challenging, but have little direct effect on the architecture chosen
for the software. Two classes of characteristics do, however, have sig
nificant impact on software architectures that are, or should be, used
for scientific applications. The first of these classes is the characteris
tics of the context in which scientific applications are implemented, and
the second class is the characteristics of the context in which scientific
applications evolve. Can you suggest others?
Implementation Context
1 Developers of scientific applications usually have deep understand
ing of the science, and deep domain knowledge. They may have
deep knowledge of the relevant mathematics. On the other hand,
they typically don't see themselves as professional programmers;
they see themselves as scientists. A consequence of this is that they
are not familiar with modern software engineering practice and
knowledge and may not recognize the need for it. As an extreme
illustration, all too often they produce little or no documentation.
2 Normally, no formal specification document exists before or even
after the application is implemented.
3 The development team often is distributed geographically and or
ganizationally, that is, there is no single organizational manage
ment in control of the development, and developers are often vol
unteers from diverse organizations.
