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\begin{tabular}{l}
{\large \bf Sponsoring Agencies} \\
\footnotesize
   \\
Advanced Research Projects Agency (ARPA)\\
Department of Energy (DOE)\\
Environmental Protection Agency (EPA)\\
National Aeronautics and Space Administration (NASA)\\
National Science Foundation (NSF)\\
National Institutes of Health (NIH)\\
National Institute of Standards and Technology (NIST)\\
National Oceanic and Atmospheric Administration (NOAA)\\
National Security Agency (NSA)\\
  \\
{\large \bf Committees}\\
  \\
{\bf Organizing Committee}\\
\footnotesize
  \\
Paul H. Smith, Chair, NASA\\
Stephen M. Griffin, Executive Secretary, NSF\\
Norman Glick, NSA \\
Fred Johnson, NIST \\
Tom Kitchens, DOE \\
Jacob Maizel, NIH \\
Tom Pyke, NOAA  \\
Joan Novak, EPA \\
Gil Weigand, ARPA  \\
Robert Voigt, NSF \\
  \\
{\bf Program Committee}\\
\footnotesize
  \\
Rick Stevens, Program Chair, Argonne National Laboratory\\
Fran Berman, University of California - San Diego \\
Al Brenner, Supercomputing Research Center \\
Phillip Colella, University of California - Berkeley \\
Kevin Drogenmeir, University of Oklahoma \\
Ian Foster, Argonne National Laboratory \\
Geoffrey Fox, National Parallel Architecture Center, Syracuse \\
Mike Heath, University of Illinois and NCSA \\
Ken Kennedy, Rice University \\
Tom Kitchens, DOE \\
Robert Martino, NIH  \\
Paul Messina, California Institute of Technology \\
Reagan Moore, San Diego Supercomputer Center \\
Dan Reed, University of Illinois \\
Ralph Roskies, Pittsburgh Supercomputing Center  \\
Manny Salas, NASA Langley Research Center \\
Joel Saltz, University of Maryland  \\
Ahmed Sameh, Center for Supercomputing Research and Development \\
Fran Sullivan, Supercomputing Research Center \\
Andy White, Los Alamos National Laboratory \\
Paul Woodward, University of Minnesota \\
\end{tabular}


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\title{\bf Workshop and Conference on Grand Challenge Applications
and Software Technology}


\maketitle




\chapter{Executive Summary}

On May 4--7, 1993,
nine federal agencies sponsored a four-day meeting on
Grand Challenge applications and software technology.

Over two hundred and fifty 
researchers 
from universities and national laboratories
attended the meeting.
The objective was to
bring High-Performance Computing
and Communications (HPCC) Grand Challenge applications
research groups supported under the federal HPCC
program together with HPCC software technologists
to
\begin{itemize}
\item
discuss multidisciplinary computational science research issues and approaches,
\item
identify major technology challenges facing users and providers, and
\item
refine software technology requirements for Grand Challenge applications
research.
\end{itemize}

The first day and a half 
focused on applications.
Presentations were given by
speakers
from  universities, national laboratories, and government agencies
actively involved in
Grand Challenge research.
\marginpar{The strategy is to bring
together
applications researchers and software technologies
to discuss high-performance computing needs.}
Five areas of research were covered:
environmental and earth sciences; computational physics;
computational biology,
chemistry, and materials sciences; computational
fluid and plasma dynamics; and applications of artificial intelligence.

The next day and a half was spent in working groups
in which the applications researchers were joined by software
technologists. 
Nine breakout sessions took place:
(1) I/O, Data, and File Systems; (2) Parallel Programming Paradigms;
(3) Performance Characterization and Evaluation of Massively Parallel
Processing (MPP) Applications;
(4) Program Development Tools; (5) Building Multidisciplinary Applications;
(6) Algorithm and Libraries I; (7) Algorithms and Libraries II; (8) Graphics and
Visualization; and (9) National HPCC Infrastructure.

On the final day,
a representative from
each of the applications areas presented a ten-minute summary 
of the common themes, approaches, and obstacles in the specific 
Grand Challenge discipline.
These were followed by thirty-minute presentations
reporting the findings of the nine breakout sessions
and 
discussing significant new opportunities for
high-performance computing and communications research.
In addition, representatives from several U.S. industries reported
on Grand Challenge efforts under way in 
such areas as global finance and chemical production.

The researchers in the Grand Challenge community
and in
the Software Development community agreed that 
the federal HPCC program is already having an 
undeniable impact.
\marginpar{High-performance computers are helping to solve
previously intractable problems.}
Indeed, one
might almost speak of a {\it high-performance revolution},
with
widespread interest in and use of scalable parallel systems by industry and academia;
increased focus on parallelism by workstation vendors;
growing emphasis on the need for standards;
and
almost universal concern for 
developing better products (machines, languages) to make parallel computing effective.

And while the researchers agreed that it
is probably too early 
to expect HPCC program activities
to have 
had a major impact on science and industry,
{\it exciting progress is being made}.
In particular, high-performance computing is enabling researchers to
attack previously intractable problems, such as the recent
$512^3$ simulation of compressible turbulence.

This increasing capability to handle complex problems has already attracted
the attention of industry.
{\it Industry involvement} 
was noted in numerous Grand Challenge projects, including
aerospace,
environmental science,
biophysics,
computational fluid and plasma dynamics, 
computational chemistry,
reservoir modeling, and
global computational finance.

Nevertheless, several 
{\it outstanding issues} were identified.
\begin{itemize}
\item
{\bf Software tools.}
\marginpar{Programming tools are essential for dramatically increasing 
scientific production on parallel computers.}
A clear need exists for
better parallel debugging tools,
tools for multidisciplinary applications,
performance-monitoring tools, and 
language support to
allow users to write programs at a higher level than currently
possible.
The cause for the poor software support 
is the fact that
the Grand Challenge grants currently focus on the
output of the applications rather than on the software to achieve that
output.
More effective mechanisms are needed for exchanging
information on tool availability and accessibility.

\item
{\bf Database management.}
The Grand Challenge studies
are generating enormous databases (up to 8 terabytes, with
predictions in the petabyte range).
Making this data useful will require
not only 
high-density, high-throughput storage devices but also 
improved archive and directory services.
Even further, specialized visualization systems will be essential
for examining the vast datasets
expected from Grand Challenge research.

\item
{\bf Data rates.}
I/O is simply not scaling 
fast enough to keep up with 
CPU speed and is threatening to become
a major bottleneck for teraflops applications.
Further research is needed to understand 
the I/O characteristics of
massively parallel computers.
Parallel I/O benchmarks are also needed that accurately reflect applications
to 
requirements.

\item
{\bf Standards.}
Current efforts such as
the evolving Message Passing Interface
standard
appear promising, but further work is needed,
for example, to promote vendor standards for archiving large amounts of
data. 
\end{itemize}

To address these problems, the workshop participants formulated
several specific suggestions:
\marginpar{Researchers have identified priorities for accomplishing HPCC goals.}
\begin{itemize}
\item
Support a ``Grand Challenge in Software Tools"
and establish a Science and Technology Center for HPCC Software Tools,
to encourage collaborative development of tools for Grand Challenge applications.

\item
Establish or encourage ``software capitalization" programs to foster the
development of research ideas and prototypes into production tools and to
ensure the maintenance of 
the software in the library.

\item
Promote the development of research tools
into usable software aids
(through an existing organization such as OSF
or a new organization or industrial consortium).


\item
Develop a parallel software tools electronic journal/newsletter (complete with an
editorial board) to publicize tool availability and tool needs.

\item
Require computer vendors (through explicitly worded RFPs) to provide
parallel debuggers, profilers, and compilers for MPP machines.

\item
Have HPCC projects explicitly promote the exchange of students and staff
between application and tool developers, to encourage computer
scientists
and computational scientists to interact more closely on Grand
Challenge projects.

\item
Increase the current federal involvement,
specifically by
providing
(1) stable base-level support for ongoing
projects and (2) stable long-term
support
for new
initiatives such as multidisciplinary programs and scalable I/O.


\end{itemize}

\newpage
\section{Program}

%From BENTON@a.psc.edu Wed Aug 11 09:27:06 1993
                    


\begin{center}
{\large \bf Daily Schedule}
\end{center}


\begin{flushleft}
Tuesday, May 4 \\
\end{flushleft}

\begin{flushleft}
8:00 - 9:00\hspace{.25in}Welcome\\
9:00 - 10:15\hspace{.25in}Application Session\\
\hspace{.15in}Panel A, Environmental and Earth Sciences (3 presentations)\\
10:15 - 10:45\hspace{.25in}Break\\
10:45 - 12:25\hspace{.25in}Application Session\\
\hspace{.15in}Panel A continued (4 presentations)\\
12:25 - 1:30\hspace{.25in}Lunch\\
1:30 - 3:35\hspace{.25in}Application Session \\
\hspace{.15in}Panel A continued (1 presentation)\\
\hspace{.15in}Panel B, Computational Physics (4  presentations)\\
3:35 - 4:00\hspace{.25in}Break\\
4:00 - 6:05\hspace{.25in}Application Session\\
\hspace{.15in}Panel B continued (3 presentations)\\ 
\hspace{.15in}Panel C, Computational Biology, Chemistry and Material Sciences \\
(3 presentations)\\
7:00 - 8:00\hspace{.25in}Reception\\
8:00\hspace{.25in}Workshop Dinner\\
\end{flushleft}

\begin{flushleft}
Wednesday, May 5\\
\end{flushleft}

\begin{flushleft}
8:00 - 10:05\hspace{.25in}Application Session\\
\hspace{.15in}Panel C continued (5 presentations)\\
10:05 - 10:30\hspace{.25in}Break\\
10:30 - 12:35\hspace{.25in}Application Session Panel C continued (2 presentations)\\ 
\hspace{.15in}Panel D, Computational Fluid and Plasma Dynamics \\
\hspace{.2in}(3 presentations) \\
12:35 - 1:30\hspace{.25in}Lunch\\
1:30 - 3:35\hspace{.25in}Application Session\\
\hspace{.15in}Panel D continued (3 presentations)\\
\hspace{.15in}Panel E, Applications of Artificial Intelligence  \\
\hspace{.2in}(2 presentations)\\
3:35 - 4:00\hspace{.25in}Working Groups (Instructions)\\
4:00 - 5:30\hspace{.25in}Working Group Breakout Session I\\
7:00 - 7:30\hspace{.25in}Buffet Dinner\\
7:30 - 9:30\hspace{.25in}Working Group Breakout Session II\\
\end{flushleft}

\begin{flushleft}
Thursday, May 6\\
\end{flushleft}

\begin{flushleft}
7:30 - 8:30\hspace{.25in}Breakfast Meeting\\
\hspace{.15in}(Chairs and Co-chairs of Breakout Sessions)\\
8:30 - 12:00\hspace{.25in}Working Group Breakout Session III\\
12:00 - 1:00\hspace{.25in}Lunch\\
1:00 - 3:00\hspace{.25in}Working Group Breakout Session IV  (Establish conclusions)\\
3:00 - 5:30\hspace{.25in}Working Group Breakout Session V (Writing)\\
\hspace{.15in}Applications (Birds-of-a-Feather) \\
7:00\hspace{.25in}Conference Reception\\
\end{flushleft}

\begin{flushleft}
Friday, May 7\\
\end{flushleft}

\begin{flushleft}
8:00 - 8:10\hspace{.25in}Welcome by Paul H. Smith,\\
\hspace{.15in}NASA, Chairman of the Organizing Committee\\
8:10 - 9:10\hspace{.25in}Summary Reports from Application Panels\\
\hspace{.15in}Moderator:  David Nelson, DOE\\
\hspace{.25in}Environment and Earth Sciences: Robin Dennis, EPA\\
\hspace{.25in}Computational Physics: Ken Kliewer, Oak Ridge \\
\hspace{.25in}Computational Biology, Chemistry and Material Sciences:\\
\hspace{.35in}L. Ridgeway Scott, University of Houston\\
\hspace{.25in}Computational Fluid Dynamics: Tom Zang, NASA Langley \\
\hspace{.25in}Applications of Artificial Intelligence: Robert Berwick, MIT \\
9:10 - 10:00\hspace{.25in}Reports from Working Groups\\
\hspace{.15in}Moderators:  Lee Holcomb, NASA; Rick Stevens, Argonne \\
\hspace{.25in}National HPCC Infrastructure: Maxine Brown, UIC\\
\hspace{.25in}I/O, Data and File Systems: Carlos Roberto Mechoso, UCLA \\
10:00 - 10:25\hspace{.25in}Break\\
10:25 - 12:30\hspace{.25in}Reports from Working Groups Continued\\
\hspace{.25in}Parallel Programming Paradigms: Kim Mills, Syracuse U.\\
\hspace{.25in}Performance Characterization and Evaluation of MPP\\
\hspace{.25in}Applications: \\
\hspace{.35in}Robert Harrison, Pacific Northwest Laboratory\\
\hspace{.25in}Program Development Tools: L. Ridgway Scott, U. of Houston\\
\hspace{.25in}Building Multidisciplinary Applications: Tom Zang, NASA\\
\hspace{.35in}Langley Research Center\\
\hspace{.25in}Algorithms and Libraries I: Phil Colella, UC-Berkeley\\
12:30 - 1:20\hspace{.25in}Lunch\\
1:20 - 2:10\hspace{.3in}Reports from Working Groups Continued\\
\hspace{.25in}Algorithms and Libraries II: Bob Sugar, University of California\\\hspace{.37in}at Santa Barbara\\
\hspace{.25in}Graphics and Visualization: Paul Woodward, University of Minnesota\\
2:15 - 3:15\hspace{.25in}Conference Address: John H. Gibbons, Science and Technology\\
3:15 - 3:30\hspace{.25in}Break\\
3:30 - 5:00\hspace{.25in}Industrial Applications Panel\\
\hspace{.15in}Moderator:  Donald Lindberg, HPCC National Coordination Office\\
\hspace{.25in}Aeronautics: Jan Tulinius, Rockwell International Corporation\\
\hspace{.25in}Chemical Products: David Dixon, Dupont \\
\hspace{.25in}Automotive/Manufacturing: Howard Crabb, Ford Motor Co. \\
\hspace{.25in}Energy Resource Extraction: James S. Nolen, Western Atlas Software\\
\hspace{.25in}Health Care: Hartwig Blume, Phillips Medical Systems\\
\hspace{.25in}Financial: David Audley, Prudential Securities\\
\hspace{.25in}Textiles: James Plouffe, Dupont\\
5:00\hspace{.25in}Adjourn
\end{flushleft}
\newpage
\section{Application Sessions}

\noindent
{\Large \bf Panel A -- Environmental and Earth Sciences}\\
(Chair:  Joan Novak, EPA)\\
                                   
\begin{tabular}{l}
Armistead G. Russell\\
A Distributed Computational System for
Large-Scale Environmental Modeling \\    
Carnegie Mellon University\\
         \\
Kenneth Galluppi and Daewon W. Byun\\
Comprehensive Eulerian Air Quality Modeling of Multipollutant\\
Species in the Atmosphere\\
North Carolina Supercomputer Center and EPA\\
   \\
Thomas O. Barnwell, Jr., and Robin L. Dennis\\
Linked Water and Air Quality Management  \\			
EPA\\
  \\
Alexander MacDonald\\
Future of High-Resolution Weather Prediction	\\
NOAA/Forecast Systems Laboratory\\
  \\
Mary Wheeler\\
Reservoir Modeling for Porous Media:  Partnership in Computational Science\\
Rice University\\
   \\
Kenneth Eggert and Shiyi Chen\\
Reservoir Modeling for Porous Media:  Lattice Gas Computations\\
Los Alamos National Laboratory \\
  \\
Dave Bader \\
Ten- and Hundred-Year Climate Prediction \\
Pacific Northwest Laboratory \\
  \\
C. Roberto Mechoso \\
Development of an Earth System Model: \\ 
  Atmosphere/Ocean Dynamics and Tracers Chemistry \\
University of California - Los Angeles\\
  \\
Dan Kowalski\\
Development of Algorithms for Climate Models Scalable to Teraflops Performance \\
NASA/Goddard Space Flight Center\\
   \\
Richard B. Rood (Presented by Peter Lyster)\\
High-Performance Computing and Four-Dimensional Data Assimilation:\\  
  Impact  on Future and Current Problems	
NASA/Goddard Space Flight Center
   \\
\end{tabular}

\newpage
\noindent
{\Large \bf Panel B:  Computational Physics}\\
(Chair:  Tom Kitchens, DOE)\\


\begin{tabular}{l}
John Gardner\\
Understanding Solar Activity and Heliospheric Dynamics:\\
Naval Research Laboratory\\
   \\
George Lake (Presenter: Calvin Lin) \\
Large-Scale Structure and Galaxy Formation\\
University of Washington\\
  \\
Robert Rosner\\
Convective Turbulence and Mixing in Astrophysics\\
The University of Chicago\\
  \\
Wojciech Zurek and M. Warren\\
Scalable Hierarchical Particle Algorithms for Galaxy Formation\\
and Accretion Astrophysics\\
Los Alamos National Laboratory\\
  \\
Bob Sugar\\
Lattice Gauge Theory \\
University of California - Santa Barbara\\
  \\
C. Grebogi, E. Ott, and J. A. Yorke\\
Controlling Chaos\\
University of Maryland\\
 \\
Richard Crutcher and Michael Heath\\
Radio Synthesis Imaging:  An HPCC Application\\
National Center for Supercomputing Applications\\
\end{tabular}

\newpage
\noindent
{\Large \bf Panel C:   Computational Biology, Chemistry and Material Sciences}\\
(Chair:  Bob Martino, NIH)\\

\begin{tabular}{l}
William Goddard\\
High-Capacity Atomic-Level Simulations for Design of Materials\\
California Institute of Technology\/
   \\
G. M. Stocks, B. N. Harmon, and J. W. Davenport\\
Computational Quantum Materials:\\
First Principles Simulation of Materials Properties\\
Oak Ridge National Laboratory, Ames Laboratory, Brookhaven National Laboratory\\
   \\
W. J. Camp, J. S. Nelson, S. J. Plimpton, and M. P. Sears\\
Computer-Aided Design of Biological and Biomimetic Materials:\\
Computational Design for Catalysis\\
Sandia National Laboratories \\
   \\
Jerry E. Solomon\\
High-Performance Computational Structural Biology\\
California Institute of Technology\\
  \\
T. H. Dunning, Jr., and A. Wagner\\
High-Performance Computational Chemistry:\\
Scalable Algorithms for Grand Challenge Applications\\
Pacific Northwest Laboratory and Argonne National Laboratory \\
  \\
Robert L. Martino\\
High-Performance Computing in Structural Biology and Medical Imaging\\
National Institutes of Health\\
  \\
D. Lansing Taylor and Scott E. Fahlman \\
High-Performance Imaging in Biological Research\\
Carnegie Mellon University\\
  \\
T. Barnes, V. Overacker, M. R. Strayer, and S. Umar\\
Computational Quantum Materials:  Quantum Structure of Matter\\
Oak Ridge National Laboratory and Vanderbilt University\\
  \\
Mike Colvin \\
Computer-Aided Design of Biological and Biomimetic Materials:\\
Pharmaceuticals and Agrochemicals\\
Sandia National Laboratories\\
  \\
R. Glowinski, J. A. McCammon, B. M. Pettitt, and L. R. Scott \\
Texas Center for Advanced Molecular Computation\\
University of Houston\\
\end{tabular}

\newpage
\noindent
{\Large \bf Panel D:  Computational Fluid and Plasma\\
Dynamics}\\
(Chair:  Lee Holcomb, NASA)\\

\begin{tabular}{l}
Carlos Felippa and Juri Toomre\\
High-Performance Computational Methods for Coupled Field Problems\\
and GAFD Turbulence\\
University of Colorado at boulder \\
  \\
Manny D. Salas\\
Multidiscipline Simulation of High-Speed Civil Transport\\
NASA/Langley Research Center\\
  \\
Kalpana Chawla\\
Multidiscipline Simulation of High-Performance Aircraft\\
NASA/Ames Research Center\\
  \\
Russell Claus\\
Multidiscipline Simulation of Propulsion Systems\\
NASA/Lewis Research Center\\
  \\
Phil Colella\\
Computational Fluid and Combustion Dynamics\\
University of California - Berkeley\\
  \\
Dan Barnes \\
Numerical Tokamak Project\\
Los Alamos National Laboratory\\
\end{tabular}

\vspace{.1in}
\noindent
{\Large \bf Panel E --  Applications of Artificial Intelligence} (Chair: Y. T. Chen, NSF)\\

\begin{tabular}{l}
Robert C. Berwick and Tomaso Poggio \\
High-Performance Computing for Learning:\\ 
Super-Human Supercomputing vs. Super-Computing for Human Computations\\
Massachusetts Institute of Technology\\
  \\
Richard Muntz (Presenter Carlo Zaniolo)\\
Data Analysis and Knowledge Discovery in Geophysical Databases\\			
University of California - Los Angeles\\
\end{tabular}


\newpage
\section{Working Groups}

\noindent
{\large \bf Group A --  I/O, Data, and File Systems}

\noindent
{\bf Technical Chair:}  Reagan Moore, San Diego Supercomputer Center\\
{\bf Applications Co-Chair:}  Carlos Roberto Mechoso, University of California, LA\\

This session covered issues relating to mass storage, high-speed file
systems, solid state disks, parallel file systems for MPP machines, parallel
I/O user libraries, mass storage management, scientific databases, and other
items relating to I/O.

\vspace{.2in}
\noindent
{\large \bf Group B -- Parallel Programming Paradigms}

\noindent
{\bf Technical Chair:}  Joel Saltz, University of Maryland\\
{\bf Applications Co-Chair:}  Geoffrey Fox, National Parallel Architecture Center\\

Choosing the right programming paradigm is important.  This session 
discussed the limitations and advantages of data parallel programming (e.g., HPF)
and other programming models, such as message passing, Fortran D, and Fortran
90.  Discussions focused on choosing the best programming system for the
type of problem.


\vspace{.2in}
\noindent
{\large \bf Group C - Performance Characterization and Evaluation\\
of MPP Applications}

\noindent
{\bf Technical Chair:}  Dan Reed, University of Illinois\\
{\bf Applications Co-Chair:}  Robert Harrison, Pacific Northwest Laboratory\\

Unlike serial computers, understanding performance of MPP applications is a
sometimes complex vital process involving scalability, balance, and the proper
use of performance analysis tools.  This session focused on the needs of
applications programmers for tools to assist in understanding the performance
of MPP applications.


\vspace{.2in}
\noindent
{\large \bf Group D -- Program Development Tools}

\noindent
{\bf Technical Chair:}  Fran Berman, University of California, San Diego\\
{\bf Applications Co-Chair:}  L. Ridgway Scott, University of Houston\\

A wide variety of software tools have been developed to aid in the
construction of parallel programs.  This session covered compilers, parallel
programming libraries, and support for heterogeneous computing and distributed
computing.  It also covered profilers, tracers, and logical debuggers.



\vspace{.2in}
\noindent
{\large \bf Group E -- Building Multidisiplinary Applications}

\noindent
{\bf Technical Chair:}  Ian Foster, Argonne National Laboratory\\
{\bf Applications Co-Chair:}  Tom Zang, NASA/Langley Research Center\\

Many modern simulation projects could greatly benefit from the combination of
simulation models that exist in many diverse computer codes.  The goal of this
session was to explore the needs of applications developers for tools that will
aid in the construction of these multidisciplinary applications.


\vspace{.2in}
\noindent
{\large \bf Group F -- Algorithms and Libraries I}

\noindent
{\bf Technical Chair:}  Ahmed Sameh, Center for Supercomputing Research\\
\hspace{.2in}and Development\\
{\bf Applications Co-Chair:}  Phillip Colella, University of California, Berkeley\\

The availability of quality numerical algorithms and libraries often limit the
pace of applications development.  This session covered the state of the art
in algorithms and libraries for numerical PDEs, linear algebra, and finite
element methods.


\vspace{.2in}
\noindent
{\large \bf Group G -- Algorithms and Libraries II}

\noindent
{\bf Technical Chair:}  Fran Sullivan, Supercomputing Research Center\\
{\bf Applications Co-Chair:}  Bob Sugar, University of California, Santa Barbara\\

Modern simulations often require new algorithms that have not been extensively
used in scientific computing.  This session covered areas like random number
generators (including parallel random numbers), multipole methods, Monte Carlo
methods, particle-in-cell algorithms, and geometric and symbolic algorithms.


\vspace{.2in}
\noindent
{\large \bf Group H -- Graphics and Visualization}

\noindent
{\bf Technical Chair:}  Paul Woodward, University of Minnesota\\
{\bf Applications Co-Chair:}  Tom DeFanti, University of Illinois at Chicago\\

Graphics and visualization are critical to modern scientific applications. 
This session focused on the graphics needs for Grand Challenge-type
applications, including the problems of data movement, parallel rendering,
portable tools like AVS, virtual reality, and other frontier issues in graphics
and visualization.


\vspace{.2in}
\noindent
{\large \bf Group I -- National High-Performance Computing and\\
Communications Infrastructure }

\noindent
{\bf Technical Chair:}  Charlie Catlett, University of Illinois at Urbana-Champaign\\
{\bf Applications Co-Chair:}  Greg McRae, Massachusetts Institute of Technology\\

Until recently the development of the national HPCC infrastructure has been
developed largely by individual institutions and funding agencies.  Since the
creation of the HPCC initiative more coordination is possible.  This session
discussed the infrastructure needs and requirements of the applications
teams.  These include high-performance networking, integration of workstations
and MPP, methods of access and allocations, support, problem reporting, and the
best way to make the national computing infrastructure responsive to the needs
of applications developers and users.


\newpage
\chapter{Background and Objectives}

In the past two years, the federal government has
funded a number of research teams 
to attack Grand Challenge problems in science and engineering.
While these problems include seemingly unrelated areas as
materials science, biology, chemistry, earth science, physics, and
artificial intelligence,
many of the obstacles faced by the researchers 
are similar.
A principal objective of the 1993 Workshop on Grand Challenges and
Software Technology was to identify these common problems
and to share related experiences.

Another objective of the workshop was to 
encourage increased communication
between computational scientists and 
computer scientists.
\marginpar{Sharing related experiences helps identify successes and future needs.}
Often,
software technologists have developed useful tools that 
the applications teams are not aware of;
and often, the applications people need special tools
that are still under development.
By bringing together these two groups,
the workshop sought to identify what enabling  
software is available and what tools are needed for
successfully solving the Grand Challenge problems.

This was the second Grand Challenge workshop
sponsored by the federal government.
The specific agencies involved were the following:

\begin{itemize}
\item
Advanced Research Projects Agency (ARPA)
\item
Department of Energy (DOE)
\item
Environmental Protection Agency (EPA)
\item
National Aeronautics and Space Administration (NASA)
\item
National Science Foundation (NSF)
\item
National Institutes of Health (NIH)
\item
National Institute of Standards and Technology (NIST)
\item
National Oceanic and Atmospheric Administration (NOAA)
\item
National Security Agency (NSA)
\end{itemize}

In the remainder of this report, we summarize the activities of the
1993 meeting on Grand Challenge Applications
and Software Technology.
These activities included panel discussions, working group sessions,
and industrial presentations.
We conclude with an evaluation of the four-day meeting and suggestions for
future meetings.


\chapter{Grand Challenge Applications Panel Sessions}

On May 4--5, 1993,
representatives from
universitites, national laboratories, and government agencies gave
presentations
about their current HPCC applications.
Five panel sessions
were held, focusing on
Environmental and Earth Sciences; Computational Physics;
Computational Biology, Chemistry, and Material Sciences;
Computational
Fluid and Plasma Dynamics; and 
Applications of Artificial Intelligence.

\section{Panel A -- Environment and Earth Sciences}
Panel A focused on
the processes 
occurring in our atmosphere,
oceans, and earth.
Using advanced computational techniques and software tools, the
researchers are developing
models
that can be used for environmental management and prediction.
Nine topics were addressed:
\begin{itemize}
\item
a distributed computational system for large-scale environmental modeling,
\marginpar{Understanding our environment requires understanding the processes
occurring in the atmosphere, oceans, and earth.}
\item
air quality management,
\item
linked water and air quality management,
\item
high-resolution operational weather prediction,
\item
reservoir modeling for porous media,
\item
Grand Challenge in climate change,
\item
development of an earth system model,
\item
development of algorithms for climate models scalable to teraflops
performance, and
\item
high-performance computing and four-dimensional data assimilation.
\end{itemize}
Despite the apparent diversity of topics,
one can distinguish four main categories.

1.  {\bf Environmental management.}
Whether the researchers are dealing with air quality, water quality, or
a combination of the two,
the issue is the same: How can we effectively use the models
(after we have developed good models)
to address practical problems such as ozone levels, acid deposition,
or air pollution?
The challenge is twofold.
First, we must make high-performance computing understandable;
this may require development of expert systems and effective
interfaces to existing visualization packages for the average user, as
well as
design of a meta-computer for the decision maker.
Second, we must learn how to use different models
for a particular environmental situation;
currently, major mismatches exist between  the spatial and temporal
systems of models.

2.
{\bf Weather forecasting.}
Despite great progress in long-range forecasting, very little progress
has been made in our ability to handle precipitation and cloud
formation.
By the mid-1990s, we expect to have a thirty-fold increase in
observational data.
\marginpar{Teraflops computers will enable researchers to consider atmospheric models incorporating more realistic conditions.}
To utilize these data effectively, researchers will need 
high-performance computing power of up to 30 gigaflops
(and the associated standards for MPP tools and languages) to enable
design of a 5-km-resolution model.

3. {\bf Reservoir and groundwater modeling.} The need for more powerful computers and standardized tools was also emphasized in the talks on
reservoir and groundwater modeling.
This Grand Challenge area deals with multiphase, multicomponent flows.
Currently, the level of application is restricted to grid blocks of
thousands of feet by thousands of feet.
For practical applications, researchers must be able to deal with
blocks on the order of centimeters.
While parallel domain decomposition solvers appear promising,
experience with running these solvers on the Intel DELTA
parallel computer
have underscored
the need for efficient, standardized parallel I/O servers.

4.  {\bf Global and earth systems.}
Finer resolution was one of several recurrent 
topics in the presentations on global and earth systems.
Oceanic models in particular require fine resolution (say, in the tens
of kilometers),
which in turn depends on the availability of teraflops computers and greatly
increased storage.
Coupled oceanic/atmospheric models raise additional problems.
Currently, researchers are
experimenting with sharing computer models at different institutions;
issues such as distributed heterogeneous computing and user traffic over
a gigabit network must be resolved, however, before such model sharing
becomes practicable.

Regardless of category,
the obstacles are similar:
\begin{itemize}
\item
domain decomposition and load balancing,
\item
parallel I/O,
\item
speedup,
and 
\item
data management.
\end{itemize}
Not surprisingly, the approaches taken to address these obstacles are
also similar (numerical
simulations of PDEs and ODEs, Navier-Stokes equations, and
spatial discretization of the PDEs), as are the numerical methods
(finite difference, finite
element,
spectral methods, and fast Fourier transforms).
The result of this similarity is not---as one might fear---duplication
of effort; rather, the result is {\it value added}:
we are seeing teams of researchers tackling many facets of the science
and computational science.

Joining these teams are a number of U.S. industries.
The active collaboration of utilities 
(including the Electric Power Research Institute and
the Tennessee Valley Authority), the automotive industry 
(including GM, Ford, and the Motor Vehicle Manufacturers Association),
the American Petroleum Institute, and the
Consortium for Advanced Modeling of Regional Air Quality
attests to the importance of this Grand Challenge research.
\marginpar{Teamwork is enabling researchers to tackle larger, more complex problems.}

And this teamwork is paying off
in the ability to tackle larger, more complex problems (such as
the ocean conveyor belt).
Indeed, the panel concluded that ``exciting progress is coming in science,
environmental management, and high-performance computing.''

\section{Panel B -- Computational Physics}

The first panel focused on understanding and managing our planet.
The second panel session addressed
the even broader topic
of understanding our universe.
Several areas of computational
physics were considered:
\begin{itemize}
\item
understanding solar activity and heliospheric dynamics,
\item
large-scale structure and galaxy formation,
\item
convective turbulence and mixing in astrophysics,
\item
scalable hierarchical particle algorithms,
\item
quantum chromodynamics,
\item
controlling chaos, and
\item
radio synthesis imaging.
\end{itemize}
The computational complexity of these problems is enormous.
\marginpar{Complex physics problems require sufficiently enhanced modeling capabilities to simulate complex problems over enormous time periods.}
Phenomena of widely different length scales are intimately
intertwined,
vast numbers of particles and vast periods of time are involved, and
thousands of multidimensional integrals must be considered.

For example, a typical astrophysical fluid dynamics problem may involve
transportation of heat energy, angular momentum,
magnetic fields, and elemental substances; 
mixing of the turbulent fluids may be driven by thermal buoyancy, semiconvection,
and gravity waves. 
Faced with
the impracticality of doing a real calculation, the researcher  
typically deals with
dimensionless parameters, identifies the smallest feasible scale, and regards
the simulation as
a type of controlled lab experiment.
Yet even such a severely restricted experiment may require a thousand
hours on a CRAY Y-MP.

Another typical problem focuses on the evolution of galaxies and
clusters of galaxies over billions of years.
Scientists must consider such questions as, Are there ``Great
Attractors''?
Are there gases or ``dark matter'' obscuring our view?
To answer such questions requires archiving terabytes of data.

From the evolution of galaxies to the development of the universe
is another step in computational complexity.
What were conditions like initially? How have they changed from the
radiation-dominated era to the matter-dominated era, and what are they
like now?
Solving the physical equations
required to answer such questions could literally take years using traditional
methods.
Researchers are therefore exploring new parallel schemes, including
treecodes and
fast
multipole methods.

Common to all these examples are six fundamental needs:
(1) portable code, (2) standards for languages and message passing,
(3) interactive
visualization tools, (4) increased computer storage, 
(5) a ``reasonable'' debugger, and (6) improved networking.
As a first step toward meeting these needs, researchers are experimenting
with remotely located telescopes connected by high-speed networks
to very high performance computers and online data archives that are
accessed over gigabit/second networks.

\section{Panel C -- Comp. Biology, Chemistry,
Materials Sciences}

Panel C combined three areas of critical societal importance: biology, chemistry,
and materials science. 
In biology,
new tools and advanced computer systems developed by
the HPCC program are enabling researchers to analyze
data from complex molecules in living cells.
In chemistry, 
massively parallel computers are being used to investigate outstanding problems
in environmental remediation. 
And in materials science, 
high-performance computing is enabling more accurate simulation
of new materials---for example, for high-temperature superconductors.

The presentations focused on six areas: 
\begin{itemize}
\item
high-capacity atomic-level simulations for design of materials, 
\item    
computational quantum materials, 
\item
computer-aided design of biological
and
biomimetic materials,
\item
high-performance computational structural
biology,
\item
high-performance computational chemistry, and
\item
high-performance computing in structural biology 
and medical imaging.
\end{itemize}
Since the initiation of the HPCC program two years ago,
more than ten production codes have
been developed in these areas, with faster throughput and increased cost-effectiveness.
A major achievement
was a 150,000,000-atom simulation of the biaxial failure of
ceramic material.
This simulation provides a macroscopic validation of the microscopic
force field.
\marginpar{Realistic modeling will be possible only with orders of magnitude increases in computational performance.}

Yet much more remains to be done.
Three example problems illustrate the complexity of the
computations being addressed.
The first example involves study of the atmospheric degradation of alternatives
to chlorinated fluorocarbons.
Such alternatives are being explored
in accordance with federal requirements to replace current refrigerants.
Typical computations involve 20 reactions, each requiring 
1,000--5,000 Cray hours, for a total of 20,000 to 100,000 CRAY Y-MP
node-hours.
The second example involves design of ductile, ordered intermetallic
(NiAl) alloys.
This problem involves 20,000 CRAY Y-MP hours per dislocation pathway.
Since each computation involves 3--5 pathways, the total execution
time
is 60,000--100,000 hours of CRAY Y-MP node-hours.
The final example involves protein engineering for bioremediation.
Native microbes often cannot use the contaminants for food;
therefore, scientists treat the microbe's enzymes to
produce a
more effective set.
The challenge here is that the enzymes may have hundreds of thousands
of atoms.
Quantum effects must be included; an approach based on classical mechanics
is not enough.
Moreover, a huge amount of Cray time is required to compute even a very small
percentage of the enzyme's activity.

Researchers are confident that their goals can be reached.
As computational power increases, quantum chemistry physical models
are improving; and
new techniques such as the force decomposition method
are proving capable of handling problems with 10,000 atoms extremely
rapidly on the Intel DELTA.
To fully achieve their goals, however, the researchers agree that they
need portable, high-performance math libraries.
\marginpar{U.S. companies are actively involved in Grand Challenge efforts.}
In particular, they need access to libraries of FFTs, random number
generators, and
fully distributed dense linear algebra solvers with flexible data
structures.

One very encouraging note is the broad involvement of the
chemical, metallurgical, petroleum, and pharmaceutical
industries in addressing these Grand Challenge problems.
Industrial corporations are
collaborating with computational chemists, biologists, and materials scientists in universities and
national laboratories.
Already, in conjunction with the semiconductor industry, these scientists
have engineered band-gaps for compounds with specified
light-emission properties.
And the potential areas of application are enormous,
including
\begin{itemize}
\item
imaging -- electron micrograph reconstruction, X-ray crystallography,
and real-time visible light microscopy;
\item
macromolecular structure analysis and prediction -- protein folding and
sequence analysis; and
\item
simulation of materials at the atomic level -- quantum mechanical
methods, classical molecular dynamics, and particle-continuum models.
\end{itemize}




\section{Panel D -- Computational Fluid and Plasma Dynamics}

Massively parallel computers and advanced software
technology are enabling
scientists to model fluid flow and
to perform design optimization of complex systems.
These new capabilities have application in such areas as
automobile manufacture,
aerospace vehicle design, and medicine.
\marginpar{Grid techniques and discretization methods appear especially promising.}

The panel on computational fluid and plasma dynamics
focused on
four specific topics:
\begin{itemize}
\item
geophysical and astrophysical fluid dynamics,
\item
multidisciplinary
analysis and
optimization of aerospace vehicles,
\item
compressible and reacting flows,
and
\item
fusion power.
\end{itemize}

Within these topics, problem sizes vary widely.
Computations involving controls are insignificant.
Problems involving structures may involve $10^5$ degrees of freedom
and require up to 1 gigabyte of memory for direct solution.
Problems involving basic fluid physics  may
involve more than $10 ^ 7$ points with as many as 20 words per point;
and more memory may be needed for calculating reacting flows.
Engineering flows may involve $10 ^ 6$--$10 ^ 7$ points with up to 
50 words per point; from $10$ to $10 ^3$ analyses are needed for
optimization, where
a typical analysis run includes as many as $10^4$ iterations.

For most of these problems, researchers use grid techniques or
discretization.
Grid approaches are principally block structured (for fluids) or
unstructured (for structures).
Discretization methods include second-order to fourth-order
finite volume (for
fluids), finite elements (for structures), PIC (for fusion),
and pseudospectral and finite difference (for geophysics).

These approaches have resulted in notable success, on a wide
variety of high-performance computer systems.
For example, researchers have carried out a $512 ^3$ simulation
of compressible turbulence on the TMC CM-5,
a time-dependent adaptive block-structured fluids computation on the CM-2,
a simulation
of an aircraft engine on an IBM RS/6000 network, and aeroelasticity
computations
on the Kendall Square KSR-1.
Such successes have attracted the interest of several industries:
\begin{itemize}
\item
The NASA computational aerosciences program has established interactions
with an
industry consortium focused on HPCC technology and is expanding
its traditional technology transfer to the aerospace industry.
\item
The national Tokamak project includes General Atomics and Microelectronics
Computer Corporation.
\item
The computational fluid dynamics and combustion project is working
with burner companies on the design of software for burner simulation.
\end{itemize}

At the same time, industrial interest has also focused attention on
serious needs for these Grand Challenge
projects.
\marginpar{Graphics techniques can accelerate the modeling process.}
Mathematical and algorithmic needs are particularly apparent.
Interdisciplinary coupling in aerospace is not well founded, especially for
ensuring stability.
Aerospace design would benefit greatly from optimization methods that
are more tightly coupled to the analysis and less ``black box.''
Engineering fluids simulations need improved turbulence models.
And plasma simulations would benefit from models that incorporate the
essential physics more economically.

From the viewpoint of the applications environment,
all projects would like to be able to run graphics code directly
on the MPP---not on a graphics workstation.
In addition,
most projects would benefit from the availability of object-oriented
scientific databases distributed across heterogeneous platforms.

A third major problem common to all these projects is dataset
management.
These studies generate enormous, often multidisciplinary datasets.
To exploit these datasets, researchers need rapid storage,
access, and perusal---preferably in real time.
Many scientists believe they would also benefit from the incorporation
of
scientific operations in a database management system.

Finally, physicists and engineers consistently noted four outstanding
problems with today's MPPs:
(1) peak vs sustained performance is too low (less than 10\% for
production codes using implicit methods on complex grids),
(2) the I/O bottleneck is severe, (3) architecture-optimized code
cannot
be generated rapidly, and (4) little hardware support exists for
timing/performance evaluation.
The solution to these problems will greatly increase the usage
of
massively parallel computers in computational fluid and
plasma dynamics applications.

\newpage
\section{Panel E - Applications of Artificial Intelligence}

This last 
Grand Challenge area was quite different from the preceding panels.
Applications in artificial intelligence do not involve
a well-defined set of matrices or equations.
Rather, they deal with an environment that can understand human speech
and language.

Two presentations were given:

\begin{itemize}
\item
high-performance computing for learning, and
\item
data analysis and knowledge discovery in geophysical databases.
\end{itemize}
Both presentations observed that Grand Challenge research is overwhelmed by a sea
of data.
To evaluate this data efficiently, one would like to have an
intelligent system that can handle English-like queries, can
``learn,''
and can work quickly.

Two early successes were noted.
The first involves development of a library of radial basis function
algorithms to adjust ``templates'' in an effort to
train a system to evaluate
``normalized''
patterns.
\marginpar{``Intelligent" systems may help evaluate data quickly.}
The second success involves construction of a database system that
can be used to examine the evolution of cyclones over time and space.

The researchers questioned whether a specially designed architecture
will
be needed for more extensive AI applications.
A special VLSI architecture has been developed, and there are
special parallel machines with fewer than 1000 nodes that use a database
foundation.
Serious questions about parallelism, scalability, and the I/O bottleneck remain to
be answered, however.


\chapter{Grand Challenges and Industrial Applications}

One of the stated goals of the
federal High Performance Computing and Communications program
is to spur gains in U.S. productivity and industrial competitiveness.
Many of the Grand Challenge applications presented at the
workshop included collaborative projects with industry.

On Friday, May 7, representatives from various U.S. corporations discussed
examples of industrial 
involvement in high-performance computing.
Corporations represented included
DuPont, Ford Motor Company, Prudential Securities, Rockwell International, and Western Atlas International. 

\section{Chemical Applications: DuPont Corporation}
Experimentalists at DuPont Corporation
rely on high-performance numerical simulations for accurately calculating the properties of molecules. 
\marginpar{High-performance computing benefits major industrial projects.}
Such information is used to
predict binding energies,
tertiary structure, enzymatic activity, operation of diffusion-controlled processes, and inhibition and promotion of DNA expression---information necessary 
in the design of
environmentally safe new enzymes.
Researchers are also interested in using accurate numerical predictions
for the design of chemical plants.
Here the Grand Challenges span several sciences, including chemistry
(thermodynamics, kinetics), chemical engineering (fluid dynamics),
engineering (structural design), mathematics (optimization), and
environmental control (waste minimization).
DuPont is also involved in ecosphere/geophysical modeling, for example,
to demonstrate compliance with environmental regulatory agency regulations
or to identify favorable locations to drill for oil and gas.
\section{Automotive and Manufacturing Applications: Ford }
Ford Motor Company uses high-performance computers in
diverse 
computer-aided engineering (CAE)
and computer-aided design (CAD).
By displaying CAE/CAD data as realistic three-dimensional objects,
researchers hope to dramatically reduce
the time required
to construct, modify, and evaluate design concepts.
In one case (a styled wheel), Ford scientists have cut the process from 24 days to 16; in another (a control arm), they reduced the process from 33 to 15 days.
In both cases,
the use of high-performance computers and advanced software
permitted the scientists to  
triple the number of elements analyzed,
thus providing a more accurate simulation.

\section{Financial Applications: Prudential Securities}
With an unpredictable number of diverse users
throughout the world,
and strict time constraints  
imposed by global markets,
Prudential Securities 
relies heavily on high-performance computers
(including IBM RS/6000 X-stations, a 72-node Intel Paragon, and
a 32-node Intel i860 hypercube).
The company
views scalable parallel computers 
as a ``computational opportunity'' for gaining a competitive
advantage in selected strategic markets.
Prudential is particularly interested in
improved software and interfaces that will
enable interactive, real-time computing on a globally
distributed network of heterogeneous parallel computers.

\section{Aeronautics Applications: Rockwell International}
Rockwell International scientists have turned to high-performance computing to
provide what they call
``affordable system optimization.''
The objective is
to develop an optimization process that
best satisfies customer needs,
at a given cost, through all
stages---from
specification of requirements, through design and manufacturing, 
to operations and support.
\marginpar{Industrial applications include reservoir modeling, design optimization, textile processing, automotive engineering, global financing, molecular modeling, and simulation of the brain.}
Rockwell
is investigating Taguchi methods
and concurrent engineering techniques
to improve
design and production process.

\section{Energy Resource Extraction Applications: Western Atlas}
The huge computational demands of reservoir simulation and seismic processing
have made
oil companies some of the earliest purchasers of advanced computers.
Western Atlas International
believes that numerical reservoir models are becoming 
even larger,
with more components, more unknowns, and a proportionate increase
in the number of runs required.
Such applications will require high-capacity parallel computers.
Effective use of these computers
will, in turn, require significant advances in
systems software---new algorithms
for solving stiff systems of linear equations,
improved load-balancing methods, 
better interprocedural analysis tools to handle very large applications
(500,000 lines of code),
interactive debuggers for each processor,
and emulators capable of simulating parallel operation on a
single-processor workstation.

\newpage
\section{Textile Applications: DuPont Fibers}
The textile industry and the DOE national laboratories have joined in a
partnership
called AMTEX.
The objective is to develop the
information technology
necessary for ``a demand-activated production system.''
According to DuPont Fibers, such a system will require advanced
systems integration and
multidisciplinary design capabilities.
The result, it is expected, will be a step-change productivity
increase.

\section{Health Care Applications: Phillips Medical Systems}
Phillips Medical Systems
scientists are addressing three Grand Challenge applications:
(1) generation of and access to very large databases with fundamental
clinical information,
(2) performance modeling of imaging and information management systems,
and (3) high-speed computations and communications
for intelligent information management. 
For example,
to make effective use of
an anatomical, neuro-functional,
physical-parameter four-dimensional atlas of the brain (covering
a scale from 1 micron to 20 centimeters and the entire human life span),
researchers need 
very fast, expert-driven database management tools. 



\chapter{Working Groups}

Following the presentations by the applications teams,
the workshop broke up into working groups
comprising both computational
scientists and software technologists.
The focus was on the software technology requirements
for Grand Challenge applications research.


\section{Group A -- I/O, Data Systems, and File Systems}

\noindent
{\bf Chair:}  Reagan W. Moore; {\bf Co-chair:} C. Roberto Mechoso


The current trends in computer hardware have resulted in CPU speeds increasingly outstripping I/O capability for
supporting both dataset sizes and dataset access rates.
Many applications
are limited by the I/O
capacity of current hardware, and compromise by working with reduced problem sizes.
Research efforts are needed for the development of high-performance file systems that integrate local
disk cache with archival storage, as well as for the development of database management systems
supporting scientific datasets.
\marginpar{Increasingly larger datasets have imposed enormous demands for efficient input/output systems.}

\subsection{Status}
The working group focused on two particular issues: data size and data rates.

\paragraph{Data Size of Applications.}
Typical current and near-term HPCC applications involve data traffic
of from 1 GB to 4 TB per run.   
Several applications envision writing out 10-GB files in the near term and
100-GB or larger files in the longer term per run.  This projection has implications for
staging areas. 
In some applicationsa, the amount of
output data produced by one run depends only on how long the
simulation is run.  In other words, the output size is directly influenced
by the CPU cycles available. 

With increasingly larger data runs possible on HPCC platforms, archival volume
requirements are increasing at least as fast as processor performance.  Current
archival sizes per Grand Challenge group typically range from 500 MB to 500 GB,
with a peak of 10 TB.  Anticipated requirements go up to 10 PB per group.

This enormous amount of data imposes more than just storage requirements.
Several simulation-type HPCC applications generate large numbers of
files ($10 ^5$) and corresponding numbers of job submissions, requiring
substantial human management time.  Some groups are attempting to alleviate
this problem by increasing staging area storage and developing better data-management software.  

\paragraph{Data Rates.}
Current and near-term HPCC applications involve I/O rates of 1--40 MB/s to disk
and 0.5--6 MB/s to archive.  
Users indicate that 1 GB/s to disk and 100 MB/s to archive will be required in
the near future.  This disk rate is achievable today on appropriately configured
CRAY C-90's, but is not achievable on most MPP platforms.  The archival rate is not
possible in any cost-effective way today.

The choice of algorithm strategy and the necessity for the recomputation of data are strongly influenced by the ratio of I/O to CPU power.
For instance, current limited I/O rates are forcing more codes to rely on ``in-core" solution algorithms.

\subsection{Targets of Opportunity} 
This section is organized top-down through the storage hierarchy: from primary
memory through secondary and tertiary storage.
\marginpar{Fast and convenient access to data---whether remote or local---is needed.}
Requisite network communication
is implicit in all levels of the system. The main theme of recent I/O research
is greater performance by increasing parallelism in all levels of the system,
that is, {\it beyond} the massively parallel processors.

\paragraph{Memory-Level I/O Research.}
Research is under way in compiler and run-time mechanisms to 
utilize the memory hierarchy more efficiently, for example, by data blocking, data placement, prefetching,
file caching, and run-time analysis of I/O access patterns.  The goal is to
give the illusion of a giant main memory by static and dynamic optimizations
that improve locality of accesses. One example is memory servers, which expand
the primary memory available to a given processor by using memory in idle nodes
within an MPP.

\paragraph{Disk-Level I/O Research.}
At the disk level the two major areas of research are high performance and schemes for storing
information found in MPPs. High performance includes I/O benchmarks, high-performance file systems for MPPs, and parallel disk organizations for MPPs.
I/O benchmarks are needed to give realistic metrics for evaluating alternatives in
trying to accelerate Grand Challenge applications. The lack of benchmarks has
inhibited progress in many fields, while good benchmarks have led to rapid
innovation. 

Research in storage schemes includes parallel file systems for MPPs, persistent
object stores, and scientific databases. The goal of parallel file systems is
to remove sequential bottlenecks between parallel processors and parallel
disks. Persistent object stores and scientific databases are new paradigms to
add type information and structure, enabling researchers to access information more intelligently and
more efficiently.

An important consideration for all the topics at this level is convenient and
fast access over local and wide-area networks for distributed data. Grand
Challenge applications have huge data repositories that must be made available
to researchers across the organization and the country.

\paragraph{``Tape"-Level I/O Research.}
``Tape''-level I/O includes all storage media accessed via robots: tape archives,
optical jukeboxes, and network-attached peripherals.  At this level, migration
strategies for network-attached peripherals are still being developed. A key
topic is extending automatic migration from local disk to the next two levels
of the storage hierarchy.  Data management issues include databases for large
and numerous objects and data organization for different access patterns. New
latency-tolerant schemes must be invented to hide the inherent latencies of
tape robots, measured in tens of seconds, so that this massive capacity is
efficiently used. A final challenge for this area is offering high-bandwidth
data transfers to the bottom of the memory hierarchy.

\subsection{Relationships}

The research topics identified in the preceding section raise pointed questions about
existing I/O policy and relationships
between applications and software developers.

\paragraph{Software Sharing.}  
Technological software-sharing mechanisms include anonymous FTP,
Gopher servers, and the hypermedia MOSAIC software.  These mechanisms are
heavily used by the community.  The near-term establishment of distributed file
systems such as AFS and DFS will further facilitate software sharing.
\marginpar{Parallel I/O benchmarks must accurately reflect applications requirements.}
The NASA
HPCC software-sharing mechanism should be more strongly promoted, and its use
by all the Grand Challenge applications groups encouraged.
 
\paragraph{Balance.}
The HPCC program has emphasized teraflops applications.  This focus has
exacerbated the divergence of CPU speed and I/O capability:
I/O is not scaling
as fast and is becoming a major bottleneck for teraflops applications.  Storage
of data associated with real-time, I/O-intensive applications will require
stronger funding support to create easily used distributed systems.  For many
applications, both the amount of data that must be stored and the rate at
which the data needs to be accessed will soon increase beyond the capability of
current systems.

\paragraph{Interdisciplinary Interactions.} 
The interdisciplinary Grand Challenge teams are helping break down sociological barriers
between computer scientists, applications scientists, and system integrators. 
Improved collaboration  between these groups can be facilitated through
development of parallel I/O benchmarks that accurately reflect applications
requirements.  These interactions can be further enhanced by considering
improvements in I/O and storage to be part of the deliverables for these
projects.    

\paragraph{Vendor Software.}  
Vendors are moving in the right direction for supporting Grand Challenge
application I/O requirements---but too slowly.  The HPCC community needs to
promote vendor standards for archiving large amounts of data.  An example is an
industry effort to define standard interfaces for databases to make use of
tertiary storage.  Commercial support for scientific databases also needs to be
encouraged.

\paragraph{Industrial Applications.}  
The very large amounts of data generated by Grand Challenge applications is forcing systems
software development for efficiently handling large objects.  Scientific
application projects  have increased the understanding of the importance of I/O
among computer scientists and between them and application scientists.
High-performance scientific databases can have a significant effect on areas outside the
current HPCC focus (e.g., medical image database, geographical information
systems).

\subsection{Findings}

The findings of the I/O working group are organized into two parts:  (1)
development of secure, robust, high-performance, high-capacity, transparent
file systems and (2) development of database management systems supporting
scientific datasets.
\marginpar{Effective I/O requires robust file systems and database management systems.}
We feel that each of these areas is important in its
own right, and we therefore rank activities within each area rather than across both areas.
We also
recognize that a great number of interdependencies exist between
areas. In particular, for many applications there is a need for
layered access to storage, which will require careful integration of many of the
components discussed below. 

The performance parameters in the following discussions are based on
requirements for the next few years. 

\paragraph{File System Development.}
The group identified four findings in this section:
\begin{itemize}
\item
Provide high-performance local file systems to keep jobs CPU bound  (rate 10--500
MB/s, size 300 GB--1000 GB).
This effort comprises
support for research and development of parallel file systems (including the
goal of standardization),
standard parallel file system interfaces from programming languages,
parallel run-time library interfaces, and 
serialization methods for files stored in parallel file systems.
\item
Develop high-performance archival storage systems (rate 1--50 MB/s, size 10 TB to 1
PB).
No archival systems are available with high-speed parallel support.  This
should be an important priority.
It will require
support for staging data before MPP execution begins,
support for subsetting of large datasets,
development and standardization of archival storage interfaces to local parallel file systems,
and support for third-party transfer to network attached peripherals and systems.

\item
Ensure ubiquitous access to conventional distributed file systems 
(rate 1--10 MB/s).
Such access includes
client-level support of distributed file systems (AFS and DFS) from within
MPPs,
interfaces and migration schemes for integration of distributed file systems
with archival storage systems,
large file capability in distributed file systems, and
scaling for support of a very large number of files.

\item
Ensure ubiquitous access to high-performance distributed
parallel file systems (rate 10--500 MB/s).
Research and development are needed in distributed parallel file systems.
Also needed is 
support for third-party access in a distributed parallel file system.

\end{itemize}
\paragraph{Database System Development.}
Five specific areas were suggested for database system development:
\begin{itemize}
\item
Support for large objects
\item
Fast access to subsets of large datasets
\item
New indexing and access methods for scientific data
\item
Interface to tertiary storage systems
\item
Language interfaces to parallel database systems
\end{itemize}

The group further suggested that a set of Grand Challenge benchmarks and sample data
be created for all levels of computer systems. This set should be made easily
available  to computer systems researchers so that they can evaluate new ideas
to improve performance of Grand Challenge applications.


\section {Group B -- Parallel Programming Paradigms}

\noindent
{\bf Chair:} Joel Saltz; {\bf Co-chair:} Geoffrey Fox
 
Choosing the right programming paradigm is important.
\marginpar{Researchers must know how to choose the right program paradigm for each given problem.}
The
Working Group on Programming Paradigms considered the advantages
and limitations of data parallel programming and other
programming models such as message passing, shared memory, and
data flow.  The discussion focused on the fit between the various
paradigms and the HPCC applications.
 
\subsection{Status }
 
The working group reviewed results of the written survey of the
Grand Challenge teams and discussed the general opinions of data
parallel programming with applications researchers in the group.
To supplement this information, the working group constructed a
set of questions with the intent of getting workshop attendees to
clarify their positions on some specific issues.  Three other
working groups with a high concentration of application
researchers were approached with the following questions:
\begin{itemize}
\item
Which paradigms do you prefer:
shared-memory (name space) process parallelism,
message passing,
data parallel, or
none of the above (give counterexamples)?
\item
Suppose that there is a ``good data-parallel (Fortran)"
   compiler to handle general unstructured and block-structured
   problems. Generalize this with large-grain-sized functional
   parallelism support for such data-parallel modules. Would this
   model be useful? sufficient?
   
\item
How much efficiency would you be willing to give up in
   exchange for a major improvement in productivity (e.g.,
   automatic parallelism)?
\item
The programming paradigm supported by KSR is an example of
   one that supports quick ports (with poor initial performance)
   and incremental code optimization and parallelization. How
   important to you is this sort of capability?
   
\item
How important is C++ functionality? Why are you using C++
   (if you are)? What particular features of C++ do you find
   useful? In particular, do you make use of inheritance?
\end{itemize}
 
 Based on all the input from the surveys and discussions, the
 working group identified the following items  as summarizing the
 current status of use of programming paradigms.
 
\begin{enumerate}
\item
No single paradigm is ``the answer," even within a single
     program.   The current software provided by
     vendors represents different programming paradigms:  data
     parallel, basic shared memory, and message passing.  There
     is no consensus among Grand Challenge applications people on
     the best model to use.
\marginpar{No single paradigm is the answer.}
     To some extent the choice is based on
     taste, but it is  also based on specific application domain
     considerations, on software availability, and on different
     types of functionality found within a given program.  The
     working group noted that there is a hierarchy in the choice
     of programming paradigms. Users express a definite
     preference for general, portable, high-level languages such
     as C++ for portions of programs associated with
     preprocessing, functional control, and postprocessing. When
     it comes to  computationally intensive parts of programs,
     however, applications researchers are more likely to use a
     language for which the compilers are more efficient, such as
     Fortran. There is a further tendency among researchers
     working on heavily used algorithms (e.g., FFT or linear
     equation solvers) to focus even more closely on efficiency
     with explicit message passing and even assembly language.
   
\item
Currently, each vendor tends to promote only a single
     paradigm (matching its architecture) and lacks the resources
     to give substantial support to multiple paradigms.
     This situation is in contrast to item 1 above. Furthermore, the
     hardware technology cycle is faster than the robust tool
     development cycle, leading to constant pressure on paradigm
     development.
   
\item
Portability of applications is an important consideration.
\marginpar{Portability is vital, in view of  the diversity of computer architectures.}
     It is not so much the case that users expect to be moving
     their codes daily, but rather that the underlying hardware
     is still in a state of flux. Users are uncomfortable with
     committing substantial optimization efforts to vendor-specific language extensions, message-passing systems, or
     assembly languages when they expect hardware changes.
   
\item
Informal standards are emerging. The need for portability
     across architectures has been noted, not only by the users,
     but also by the vendors.  As a result, informal volunteer
     efforts have emerged during the past year to address this
     issue.  The High Performance Fortran Forum (HPFF) has
     drafted a data parallel programming interface which will be
     common across multiple vendor architectures.  This effort is
     expected to continue to extend the class of algorithms and
     data structures addressed.  Following the HPFF model, a
     similar effort is under way to provide a standard message-passing interface (MPI). These efforts have fairly wide
     participation and user interest.
   
\item
Rich data and operation abstractions are getting increasing
     attention because of the resulting code simplification and
     reuse. C++ is being used by several projects, and there are
     reports of  substantial productivity gains due to the higher
     level of abstraction.
 
\end{enumerate}
\subsection{Targets of Opportunity}
 
The working group identified several targets for new
research directions.
 
\paragraph{Conducting Empirical Studies of Programming Paradigms.}
Many paradigms suffer from the lack of critical evaluation.
The studies proposed here should involve the porting of
realistic applications and should be carried out in close
collaboration with applications researchers. The results
would answer many questions regarding the suitability of
programming paradigms for both general use and for specific
topics, and could be used to drive the development of the next
generation of computer software.

It is important to note that
some of these studies may fail to produce usable codes; from
the viewpoint of developing programming paradigms, such
negative results are as important as producing good codes. In
order to come to a broad understanding of the applicability of
programming paradigms, the studies should also include
industrial and commercial applications.
 
\paragraph{Achieving Better Performance with Data Abstraction.}
A wide range of Grand Challenge researchers expressed strong
interest in the use of data abstraction.
\marginpar{Paradigm studies must include realistic industrial and commercial applications.}
The need for data
abstraction is particularly pressing to researchers who solve
problems with complex hierarchically defined data structures
such as structured adaptive grids or irregular tree-type data
structures found in fast multipole algorithms.  Most Grand
Challenge applications scientists who employ data abstraction
use C++. Unfortunately, current compilers typically do not
generate efficient code when confronted with abstract data
types and inheritance. (Fortran 90 also supports many aspects
of data abstraction, but there is little experience with this
language in the Grand Challenge community, because of the poor
availability of full Fortran 90 compilers. Moreover, some of
the applications researchers are exploiting the C++ feature of
inheritance, which is not supported in Fortran 90.)
 
\paragraph{Supporting Incremental Optimizations.}
The ability to port a code to a new architecture quickly and
optimize for performance later is very attractive to users.
For example, this is possible in shared-memory programming
paradigms, such as those supported by Kendall Square Research,
software implementations of virtual shared memory, or (future)
compilation methods. For this approach to be
successful, users need to be able to run with realistic data
sets and obtain feedback that they can use to determine how to
optimize performance. Tools and techniques that facilitate
incremental performance optimization are potentially
important.
 
\paragraph{Providing Language, Compiler, and Programming Paradigm Support for
   Irregular, Block-Structured, and Adaptive Problems.}
A large fraction of Grand Challenge applications  surveyed
make use of sparse data structures or linked lists (16 out of
28 teams). In addition, 12 teams employ block-structured
grids, and 4 teams make use of nonuniform tree-type data
structures for fast multipole algorithms. Also, dynamic
adaptivity is growing in importance. In order to prove
attractive to a broad range of  Grand Challenge researchers,
programming paradigms and associated compilers and run-time
support will have to be able to effectively handle irregular
problems. Continued research on programming paradigms,
compiler techniques, and run-time support aimed at such problems
is needed.
 
\subsection{Relationships}
 
Many working group members from computer science emphasized the
positive influence work with applications people has had on their
research. This included supplying practical problems and
providing valuable feedback. The feeling of the Working Group is
that computer science and applications should be tightly
intertwined. However, there is also a significant feeling that
much of the current interaction is only token discussion. Instead
of this, real interaction through day-to-day contact is needed.
\marginpar{New incentives are needed to encourage interdisciplinary collaborations.}
For this to happen, both computer science researchers and applications
scientists must adapt. Computer science people must be willing to rapidly
prototype relatively prosaic (at least not leading-edge) systems,
and applications scientists must be willing to tolerate the
occasional system error.
 
A fundamental question raised in the working group was, {\it What is
the purpose of Grand Challenge grants?} The Grand Challenge
applications described at the workshop focused on the output of
the applications rather than on the software used to obtain that
output.
In these cases, the computer science component was often regarded as
peripheral to the main goal of the grant.
Applications
specialists, who are judged on the quality of their science, are
understandably reluctant to wait for computer scientists to
develop general, robust systems. In HPCC projects where computer science is
more central, the opposite problem may occur---scientific results
may become peripheral. This reflects a more general problem:
computer scientists and applications experts have different
professional goals. Applications scientists must develop new
scientific results, while computer scientists must focus on
general computational principles. However, the funding agencies
might improve collaboration by clearly specifying the equal
importance of computer science output and applications science output in
evaluating grant results. Also, new incentives are needed to
encourage interdisciplinary collaboration.
 
Another way to improve the effectiveness of computer science researchers in the
programming paradigm area would be to increase the visibility of
programming language research outside the computer science community. This
could lead to more use of particular programming paradigms to
express advanced algorithms, resulting in better evaluation of
new languages and systems.
\marginpar{The federal HPCC program has heightened interest in parallel computing.}
It would also help ensure that new
language developments would be relevant to the needs of the
applications scientists.
 
It is worth noting that computational scientists developing
numerical algorithms face similar problems. One working group
member reported that his group minimized this problem by having a
``captive" applications expert for doing evaluations.
This enabled
them to attack hard problems all the way from the problem
definition to its implementation. This may provide a model for work on parallel
programming paradigms.
 
\subsection{Impacts}
 
Perhaps the overriding sense of this working group was that, at
this point, it is really too early to expect significant impact
from the HPCC program outside of the program itself. That said,
however, it is important to make a few observations about the
potential impact of the HPCC program.
 
It is clear that the HPCC work has heightened interest in
parallel computing, and, as a result, it has increased the
pressure on hardware and software vendors and the computer
science research community to produce the products
(machines, languages, tools, etc.) that will make parallel
computation effective. It has also led to increased interest
in standards, which will permit application developers to
build programs based on paradigms and software
infrastructure that will be supported on a large number of
different parallel computing environments.
\marginpar{Experience in program paradigms must be transferred to aerospace, petroleum, and pharmaceutical industries.}
Examples include
High Performance Fortran (supporting the data parallel
paradigm) and the evolving Message Passing Interface
(supporting a process parallel paradigm) standards. In the
future, this sort of technology should have a major
beneficial impact on non-HPCC computing.
   
While the working group has no direct information concerning
the amount of technology transfer to date, it is clearly
important to transfer the HPCC community's experience with
parallel paradigms to those performing high-end computation
in industries such as aerospace, petroleum, and pharmaceuticals.
Many of these industries depend heavily on
solving computational problems that are quite similar to
those attacked by HPCC researchers, and they can benefit
from knowledge of what has worked well for HPCC researchers,
even if they are not in a position to make direct use of
application software developed in the HPCC program.
 
\subsection{Findings}
 
The working group made the following suggestions:
\begin{enumerate}
\item
Methods should be developed to allow mixing of different
   programming paradigms and languages in a single framework.
 
   Examples of such mixing might include standard ways of calling
   message-passing Fortran from C++, invoking message-passing
   code from HPF, or coupling a heterogeneous set of data
   parallel programs. At the current time, vendors do not view
   the support of mixed-programming paradigms as a particularly
   high priority.
\marginpar{Mixed programming paradigms hsould be integrated into a unified framework.}
   Researchers interested in multidisciplinary
   problems also expressed a strong interest in large-grained,
   process-level parallelism with subordinate data-parallel
   modules.
 
\item
``Templates," which capture key computational, communication,
   and I/O characteristics of important application codes, should
   be written and made widely available.
 
   Templates are well-structured, documented algorithms to solve
   key computational problems. These templates could be used to
   carry out  research to develop and evaluate programming
   paradigms and for multiprocessor performance studies. Several
   efforts similar in spirit to this are already under way,
   including the PBWG group collecting parallel benchmarks, but
   additional efforts should be encouraged.
 
\item
Widely available, modular components for software tools should
be developed to speed the implementation of new programming
paradigms.
 
Measures are required to reduce the time and the costs
associated with tool and compiler development because of the
rapid pace of hardware development.  Examples of such projects
include the ARPA initiatives to develop a common set of
parallel compiler run-time support routines and to develop a
compiler infrastructure.
 
\item
New incentives should be developed to encourage computer
scientists and application scientists to interact more
closely.
 
These incentives could include new awards, sabbatical-in-place
arrangements to allow interaction with applications groups, or
specific funding of software development for particular
applications. The model of having an ``outside" member within
the group should also be considered. Most important, grants
must recognize that computer science and application science
results are equally important (and the problems equally
challenging).

\end{enumerate}
 
\section{Group C -- Performance Characterization and Evaluation}
 
\noindent
{\bf Chair:} Dan Reed; {\bf Co-chair:} Robert Harrison

What sets performance tools apart from other software tools in the HPCC arena?
The answer is
simply that performance is the ultimate reason for using massively parallel
architectures in the first place. Either absolute performance (solving larger
problems faster than previously possible) or price performance (solving the
same problems less expensively) is ultimately the driving rationale for using
MPP systems.
\marginpar{To understand the performance of massively parallel processing applications requires tools focusing on scalability, load balance, and program performance. }
Thus, achieving a reasonable fraction of the potential peak
performance of such a system is an essential part of the code development
process.
The development of stable performance analysis tools represents an
enabling link in the evolution of an application from a crude parallel model to
an efficient research or industrial code. However, in order for such tools to
be used with confidence by a broad user community, they must move beyond
research prototypes to become robust, commercial-quality products.

\subsection{Status }

Many tools are available for evaluating performance on a single processor, a
few tools for a moderate number of processors, and almost no tools for a large
number of processors. The problems plaguing Grand Challenge applications
developers also hinder the rapid development of robust performance tools:
\begin{itemize}
\item unreliable hardware,
\item immature system software, and
\item incremental delivery of software.
\end{itemize}

Many researchers are investigating research aspects of performance evaluation
for massively parallel systems. However, not all research groups have the
expertise or interest in producing distributable software, and few of the
groups that do have the interest and expertise have the necessary financial
support and personnel to produce complete, robust tools.

Vendors are announcing new architectures so rapidly that their internal
performance tools group have difficulty producing robust, effective tools---both
from rapidity of development and from difficulty in securing the necessary
resources. Some of the vendors realize this problem and are reaching out to the
research community, but there have been few successful technology transfers of
high-quality software from academic or national laboratory research groups.

The result is a paucity of robust performance tools for popular
parallel systems. The perception of the (potential) user community is that this
problem is even worse than it is, with little awareness of what tools are
available.
For example, there are several tools for balancing loads across
processors, yet several users at the workshop mentioned the need for such tools.
This situation is exacerbated by difficulties in disseminating
performance analysis software:
\begin{enumerate}
\item No mechanism exists for exchanging information on tool availability  
and accessibility.
\item Significant restrictions (stemming from the current legal and funding 
environment) do inhibit the dissemination of software.
\item
Even the research groups with the interest and staff to
develop near-production-quality tools may not have the interest and staff to 	continue
maintaining and developing these tools,
	handling user comments and complaints, etc.
\marginpar{Information about new tools must be dispersed quickly, to enable researchers to exploit new architectures.}
\item Many application groups and computer centers do not have the 
resources/staff to investigate what tools would be useful for the local 	user
community, to install tools, and to support local users.
\end{enumerate}

\subsection{Targets of Opportunity}
The working group identified four areas meriting special attention.

\paragraph{Support for High-Level Languages.}
High-level programming languages present special needs for performance
measurement tools to support source code optimization by applications
programmers. Also, the user interface to performance measurement tools should
be accessible through high-level languages. An opportunity exists for an
enhanced relationship between measurement and languages to provide the user
with a more powerful program development environment. Requirements include
\begin{itemize}
\item measurements correlated with high-level semantic constructs of
emerging high-performance computing languages,
\item compiler optimizations traceable to source code and 
	performance implications provided to the programmer,
\item automated instrumentation for new languages,
\item languages/directives for explicit control of measurements, and
\item hardware and software support for access to hardware performance 
instrumentation.
\end{itemize}

Examples of these facilities exist, but they should become standard within HPCC
languages. The goal is to make measurement information on MPP performance as
accessible as symbolic debugging information is on conventional workstations.

\paragraph{I/O and Distributed Systems.}
The performance of HPCC systems is increasingly
affected by disk I/O and
network I/O to external systems
such as mass storage, visualization devices,
workstation clusters, and other heterogeneous components.
Conventionally, consideration of these I/O capabilities has
been segregated from performance evaluation of computer systems. The large
datasets of Grand Challenge problems and the trend towards application of
distributed systems require
evaluation of
external support systems
in combination with primary MPP computers.
\marginpar{Needed are performance analysis tools that consider the effects of distributed resources.}
Application programmers
must be provided the means for measuring performance parameters affecting their
program execution, even when operation is on a remote subsystem.
Both processes and data may be distributed among heterogeneous processing
platforms.  The resulting data accesses or the process communication can
limit the overall performance.
Access to I/O and remote subsystem measurement tools is essential for
application programmers to determine the causes of performance
degradation.
Additionally, distributed resources tend to be
shared, and delays caused by the resulting contention must be included.

\paragraph{Open Performance Tools and Infrastructure.}
One possibility for increasing the impact of current performance tools is to
make them easier to use and more readily interoperable within an open
performance system infrastructure. Higher-level languages, more complex
component modules, and heterogeneous systems are being used in the construction
of application codes; current performance tool technology must be applicable to
these new target areas. Furthermore, access to performance data should be
provided by language, module, and system developers for integration into open
performance measurement systems.

In contrast to performance tool standardization, we advocate an investment in
performance tool infrastructure development with an emphasis on open
technology.
\marginpar{Good tools are better than standard tools.}
It is better to have good tools than standard tools; but if tools
are made interoperable through open interfaces, then good tools could be more
easily applied to new target areas. Specifically, we suggest
\begin{itemize}
\item interfaces to performance measurements (developed by application and system software module developers);
\item performance instrumentation and database components, integrated
	with compilation systems;
\item development of scalable performance presentation technology,
accessible to performance tool builders; and
\item specification of a default level of basic performance measurement
	and analysis functionality for parallel systems, providing a basis
	for transportable performance tools.
\end{itemize}

\paragraph{Performance Characterization, Modeling, and Experimentation.}
Performance analysis has both short-term and long-term goals. Typical
short-term goals are to help write and tune programs that run fast for current
problems on current computer systems, and to establish procurement criteria.
One long-term goal is to produce programs that will also perform well in the
future (e.g., for larger or longer problems on computer systems with more
processors or different computation/communication tradeoffs). A complementary
long-term goal is to help design computer systems that will effectively support
future applications. 

All of these goals require determining the performance characteristics of
application programs and computer systems.
However, currently available tools and
techniques provide only a subset of the capabilities
needed to meet these goals.
\marginpar{Current tools are usually specialized.}
These tools
are generally
restricted to empirical measurements of a particular combination of code,
system, and sample problems.
Little or no support is provided for developing
predictive models of application performance, or even for acquiring the
information needed to construct such models.
Additional work is needed characterize massively parallel systems and
extract application requirements, as well as to construct and validate
models.

These gaps can be filled, in part by applying existing techniques to a wider
range of applications and systems, and in part by further research
collaborations between computer science researchers, application developers,
and vendors. Our major suggestion is that
\begin{itemize}
\item each major MPP class and application community should be supported by at
least one research group chartered specifically to characterize the performance
of important applications on that MPP class, both now and with an eye toward
future scalability, and to develop the tools and techniques necessary to
support such characterization.
\end{itemize}

\subsection{Relationships}

Are computer science people developing the right tools?
The answer to this depends on whom one asks.
Performance tools such as PICL and Paragraph from Oak  Ridge
National Laboratory, PRISM from Thinking Machines Corp., or ATEXPERT from Cray
Research have many enthusiastic users in the HPCC community.
On the other hand, some respondents to the workshop
survey believe that the state of programming tools is dismal. Possible
reasons for this attitude might be that
\begin{itemize}
\item parallel tools, like parallel programs, tend to be quite complex;
\item tools are often geared to system developers rather than applications developers;
\marginpar{User-friendly tools are essential to increased productivity.}
\item attention spans for learning or relearning tool use are notoriously
short;
\item scalability of performance analysis tools to large numbers
of processors is an open research question;
\item it is very hard to make a tool friendly to all users;
\item users are quick to blame the tool (or tool maker) for any problem they
encounter; and
\item new tools usually lack adequate documentation.
\end{itemize}

The working group did suggest several 
new ways to improve collaborations:
\begin{itemize}
\item Funding from government agencies is needed to enable applications users,
tool developers, and  representatives from the vendor community to work
together to develop and refine tools  to make applications developers more
productive.
\item Technology transfer programs such as that fostered by the Software
Capitalization Program from NSF to enable developers to transfer tools from a
research environment to the market place, and therefore to the applications
developer.
\item Funding, such as ARPA fellowships, that would enable computer science
students to spend 6--8 weeks with applications developers, or students in a
scientific field to work with tool developers.
\item Education projects for users, such as a catalogue that summarizes 
available tools along with extensive help in the uses of the tools, possibly
accompanied by a videotape containing software demonstrations.
\item Education projects for tool developers and performance tool users to
determine real user needs that would drive development.
\end{itemize}

\subsection{Impacts}

The HPCC program has already had a dramatic impact on computer science research
in general, and performance tools in particular.  One measure of this impact is
the widespread interest in MPP systems among academic computer scientists,
which is in marked contrast to their general lack of interest in the previous
generation of vector supercomputers.  Applications code developers and users,
on the other hand, are also interested in exploring MPP systems, but many
remain to be convinced of their practical utility.
\marginpar{For the first time in more than a generation, computer scientists and applications scientists have a common interest---MPP systems.}
One benefit of this renewed
interest in leading-edge, high-performance architectures is that for the first
time in more than a generation, computer scientists and applications scientists
have a common interest---namely, MPP systems---that could serve as a basis for
substantial collaboration and cross fertilization. Performance analysts and
tool developers can play an especially critical role in broadening the
acceptance and effectiveness of MPP systems for large-scale applications.
Because of the immaturity of algorithms and systems software for MPP platforms, current
users need all the help they can get in realizing the high performance offered
by such architectures.  Moreover, many potential ``converts'' to MPP in
industry are reluctant to take the plunge until they know that a mature
environment (including development tools and/or third-party applications codes)
will be available to enable them to use these systems effectively. 

Most potential industrial users are not inclined to invest time or financial
resources in a parallel machine without some proof of principle that they will
receive adequate return through enhanced performance.  One approach might be a
collaboration among a tool developer, a scientific research group, and
industrial partners in the co-development of both an application of interest
and an emerging performance analysis tool.  Such a collaboration would provide
the tool developer with vital feedback on the efficacy of the tool, while the
application developers benefit from the performance tuning that the tool
enables.  This symbiosis would address one of the main problems noted in our
discussions, namely, that applications developers simply either do not know
about performance tools or do not know how to use them.  Remedying this
situation could have a strong impact on applications developers within HPCC and
on the competitiveness of U.S. industry as more companies become convinced that
large-scale applications codes can be addressed effectively on massively
parallel machines.  Performance characterization can also play a critical role
in the procurement process, particularly in facilitating an appropriate match
between applications requirements and machine characteristics. 

\subsection{Findings}
We summarize the suggestions of
the working group participants as follows: 
\begin{itemize}
\item HPCC projects should explicitly fund the exchange of students and staff
between application and tool developers. Such exchanges  will provide training for both
groups, influence the development and use of performance analysis tools, and
establish long-term research collaborations.
\marginpar{Technology transfer is a key aspect of developing programming tools for massively parallel computing.}
\item The collection, maintenance, and distribution of a catalogue of
performance tools and demonstrations should be a separate and explicitly funded
activity.  This includes tutorials on basic concepts of performance evaluation,
as well as identification of actual tools for specific purposes.
\item A permanent funding mechanism (similar to the NSF Software Capitalization
Program) should be established to bring software tools to the point where
technology transfer is both feasible and economically attractive.
\item Collaborative development of tools to construct predictive performance
models for scalability of Grand Challenge applications should be supported. 
Such models are vital in reducing risk and development cost in achieving high
performance on current and future architectures.
\item In tool R\&D, more emphasis should be placed on integrating support for
I/O and networked services, in anticipation of these activities becoming more
critical to applications.
\end{itemize}



\section{Group D -- Program Development Tools}
  
\noindent
{\bf Chair:} Fran Berman; {\bf Co-chair:} L. Ridgway Scott
  
A vast array of tools exists to support parallel computing.
These tools come from three different sources---computer vendors, the
research
community, and commercial software companies. 
The
existing tools fall into a number of categories:
\begin{itemize}
\item
node compilers (e.g, native compilers, AC);
\item
parallelization support tools, including data decomposition and mapping tools

(e.g., FORGE90, KAP, PYRROS, HeNCE);
\item
debuggers (node and parallel debuggers);
\item
parallel programming languages (CM-Fortran, Fortran-M, Fortran-Linda, HPF, PC
 N,
pFortran, Strand, etc.);
\item
communication libraries (active messages, CMMD, Express, NX/2, PICL, PVM, p4,
TCGMSG, etc.); 
\item
program/performance analysis  tools
(e.g. IPS2, PABLO, Paragraph, Upshot, etc.); and
\item
math libraries (e.g., ScaLAPACK).
\end{itemize}

\subsection{Status}
The
tools frequently do not address the problems of interest to application
programmers, do not function as advertised, and/or do not deliver a significant
fraction of the performance available from the computer.  
The availability, performance, and spectrum of tools for 
parallel computers must be improved across the board:
existing tools must work and be robust;
new tools must be developed to support parallelization, 
MIMD programming, etc.
The poor quality or lack of these tools inhibits the growth and extent of the high-performance
 computing community.

A uniform complaint voiced by the group was that the software tools provided by 
 the
MPP computer vendors are grossly inadequate.
\marginpar{We need enchanced mechanisms to turn prototype tools into
well-developed tools with a broad user base.}
The core set of tools available 
 on
MPPs---operating systems, compilers, debuggers, performance analysis
tools---is
not up to the standards of robustness and performance expected for
commercial computers.  Tools are nonexistent or, even worse, do not work.
Certain classes of tools were singled out as being in a particularly poor
state. Parallel debuggers received the most criticism.  Parallel math 
 libraries
were thought to be
almost as bad, though math libraries are essential for building a
mature application software base for parallel computing.

Commercial and public domain sources have tried to fill the gaps left by the
computer vendors, but have had varying levels of success. 
Many active research projects are also producing potentially useful tools,
but these
efforts are 
available only in prototype, or 
fragmented and buried inside various application efforts. A key
issue for this working group was how to develop these technologies into effective
tools.

\subsection{Targets of Opportunity}
Many tools for parallel software development have been created; however,
application programmers often do not know which tools might be applicable, how
to get the tools, or how good they are.  We must develop
and strengthen mechanisms to turn
research prototypes into well-developed tools with a broad user base.

\paragraph{Basic Tools and Libraries.}
The primary target of opportunity is to develop robust, effective versions of
basic tools, many of which purportedly exist but are not usable.
This suggestion is in
contrast to an approach that looks for sophisticated ``magic bullets" such as
HPF---an approach greeted with considerable skepticism by 
application programmers within this group. 

\paragraph{Infrastructure.}
A mechanism for the exchange of high-performance software is critical. This 
 mechanism must
be aggressively publicized, nurtured, and used. Support and 
maintenance for tools in this
exchange {\it must} be addressed to make this an effective mechanism for 
 software
sharing. 

\paragraph{Distributed, Network, and Heterogeneous Tools and Libraries.}
One area in which new tools are seen as necessary by both users and computer
scientists is the burgeoning distributed, network, and heterogeneous (DNH) 
 arena for high-performance computing applications.
A clear target
of opportunity is to develop software tools based on experimental
efforts in DNH. Homogeneous systems and very heterogeneous systems represent
distinct targets of opportunity
because of the fundamentally distinct research issues they 
 raise.

\paragraph{Distributed Data Management Tools.}
A critical area of need voiced by almost every HPCC applications group is 
 tools
to support distributed data management.  Significant attention must be devoted
to the development of these tools.

\subsection{Relationships}
Applications programmers need parallel software tools that are robust,
 portable,
and easy to use. In addition, the tools must be very well supported, with 
 clear
value apparent to the users within a short evaluation time. This level of
sophistication requires a substantial development effort.

Unfortunately, it has fallen to the computer science research community to 
 develop
these tools. 
\marginpar{Computer scientists must be rewarded for delivering the
software that makes state-of-the-art computers usable.}
We say ``unfortunately'' because
academic computer scientists are rewarded for basic research, not
for software development.  Furthermore, computer scientists are not always
aware of the problems of interest to application programmers. As a result,
a large gap exists between the invention and utility of applicable software 
 tools. 

We believe that the current infrastructure for parallel software tool
distribution is fundamentally flawed.  Computer science researchers do not 
 get
credit or financial support for providing robust well-supported tools.  The
computer vendors are driven by high-margin hardware sales, with software lost
in the shuffle.  There are rewards for delivering the latest and 
 greatest
massively parallel computer, but not necessarily in having the software to 
 make
the machine really useful.  Furthermore, the small software vendors are 
 limited
by having only a small HPCC market on which to focus.

The result is a gap between the research arena
and the marketplace.  Therefore, one of our key suggestions
is to facilitate technology transfer by providing 
or strengthening a supporting
infrastructure.
Several ways exist to do this. The
current approach supported by the HPCC program is to encourage 
interdisciplinary teams; this activity should be expanded.
In addition, there must be a mechanism for making tools widely available.  
 The
National Software Exchange is correct in spirit, but it must be an active 
 agent
rather than a passive one. Moreover, it should provide adequate mechanisms 
 for
storing codes that have to be supported and updated (tools) as well as
codes that do not.

\subsection{Impacts}
Researchers have begun to think in terms of very large problem sizes and
datasets because of HPCC.  In software tools development, researchers are
attacking problems inherent to large concurrent systems.

\paragraph{Near- and Long-Term Impact.} 
Currently, individual application groups are maintaining their own
communications libraries and have been layering their own 
environment-specific tools 
 on
top of them.  Duplication of effort abounds.  Effective basic software
tools will allow higher-level software to be developed and will expedite
applications research.

In the long term, effective software tools will facilitate the development of
libraries serving as a solid base for technology transfer.  Although not
sufficient, this is necessary for technology transfer outside of the HPCC
program.

\paragraph{Applications' Impact on Computer Science.}
The computer science community believes that 
there has been a shift in the emphasis
of research funding.  Fundamental research has lost ground to directly
applicable research in today's zero-sum funding environment.  The question
arises as to whether we really
understand the long-range impact of an implicit
reduction of funding for basic research.

\paragraph{Industry's Impact on Applications.}
Vendors are driven to deliver hardware before the software is usable.  This
situation results in unstable platforms, which in turn have a negative impact
on applications.
\marginpar{A stronger effort is needed to transfer research results to
both computer vendors and users.}
Tool and applications developers spend an inordinate amount
of time and effort overcoming the hurdles presented by an immature system. 

\paragraph{Applications' Impact  on Industry.}
Industry will be interested in high-performance computers only when 
 interesting
applications are possible on them.  Vendors have already used mature
applications to market vector supercomputers.  It is reasonable to assume 
 that
applications being developed now will be used in a similar and effective
manner.

\subsection{Findings}
Our suggestions for improving HPCC software tools fall into two broad
groups.  The first group is concerned with infrastructure and technology
transfer mechanisms; the second group consists of suggestions for
specific tools.  

\paragraph{Technology Transfer Infrastructure and Mechanisms.}
A theme throughout this meeting was the need to enhance the
infrastructure and mechanisms to transfer technology from the computer
scientist to the applications programmer. A key component of this is
 software
sharing.  For a truly effective software sharing system, it
must have the following
 features:
\begin{itemize}
\item
A software-sharing library must be funded to include people who maintain and
support the software in the library. Note that the HPCC Software Exchange as
proposed does not provide such a solution.
\item
A parallel software tools electronic journal/newsletter (complete with 
editorial board) should be developed to include codes, tool announcements, 
 and
publicity.  The
newsletter needs to be widely distributed within the community.
\item
Canned demonstrations of tools that are ready for use outside of their
development community should be available in software-sharing libraries, so
that application users can evaluate tools quickly.  
\end{itemize}
A great deal of discussion focused on the need for a smoother technology-transfer
from research to the computer vendors as well as to the users.  A
``bridge" process is needed and should be funded by HPCC.  It is 
critical that some organization take appropriate research community tools and
develop them into robust prototypes, providing support, maintenance, and
documentation.
Two possible options for the ``bridge organization'' are to
\begin{itemize}
\item
fund and expand the charge of 
an existing organization such as  OSF, the national laboratories, or the
NSF supercomputer centers; or
\item
set up a new independent organization or industrial consortium. 
\end{itemize}
The vendors must be strongly encouraged to build and support tools---especially
stable and adequate tools which would serve as a basis for
effective high-level tools.
\marginpar{Vendors must be strongly encouraged to provide adequate tools.}
We present two specific suggestions to 
facilitate this
increased support: 
\begin{itemize}
\item
Word RFPs to require computer vendors to provide high-performance
compilers, and usable tools for MPP machines.  Good tools should be made a 
 requirement
for the vendors, not an option.
\item
Encourage the commercialization of tools that have had success in the research
community.  
\end{itemize}
Other ideas to improve the availability and quality of high-performance 
 software tools
include the following:
\begin{itemize}
\item
Establish or fund existing ``software capitalization" programs to foster the
development of research ideas and prototypes into production tools.
\item
Fund a ``Grand Challenge in Software Tools."  This would recognize that basic
tools for MPPs are needed across all applications.  The whole life cycle of
tools is a difficult process and should be recognized as a Grand Challenge in
and of itself.
\item
Fund a Science and Technology Center for HPCC Software Tools.
\item
Change the reward system for tools.
For example, HPCC could sponsor
a tools competition that would have prestigious awards analogous to the Gordon Bell
prize.  Applications users would be the judges, and the tool builders would
have an additional incentive to work on robust, usable tools.  
\end{itemize}

\paragraph{Software Tool Development.}
The utility of advanced software tools is seriously undermined by the poor
state of the basic software tools on MPP computers.  This includes node
compilers, operating systems, and basic communication routines.  Any
suggestions in this section are moot unless these issues are addressed.

We have identified both short-term and long-terms needs for HPCC 
 software
tools.  Therefore, we make the following suggestions:  
\begin{itemize}
\item
Elevate the quality and performance of the basic HPCC tools such as node
compilers, debuggers, and profilers.  
\item
Encourage standards efforts where appropriate.  For example, the
efforts of the Message Passing Interface forum will address an important and
immediate need for enhanced software portability. 
\item
Fully fund heterogeneous computing in all of its aspects  including 
 application
heterogeneity, resource management, data management, network management, and
matching of tasks to machines.  Some of this research needs to be treated as
``big science," with mechanisms for large projects that include software,
applications, and network research. 
\item
Fully fund parallel debugger efforts. Such efforts must be funded for all
phases of the life cycle---fundamental research, prototyping, and development
of robust tools and standard debugger interfaces. MIMD and metacomputer
debuggers need to be emphasized.  
\item
Develop a compiler infrastructure for making visible the compiler's internal
representations and transformations to other software tools. 
\end{itemize}
Finally, further research is required to develop 
distributed data management tools;
parallel application libraries;
data abstractions for parallel architectures;
easily layered, object-oriented data abstractions for parallel architectures;
multimedia tools for parallel systems; and
operating systems for parallel architectures.






\section{Group E -- Building Multidisciplinary Applications} 

\noindent
{\bf Chair:} Ian Foster; {\bf Co-chair:} Tom Zang


This working group addressed the systems integrations issues
associated with ``multidisciplinary applications," in which either
several different ``disciplines" are coupled in a single modeling
system (e.g., fluids and structures in an aeronautics code) or
several different ``levels" of analysis are combined within a single
``discipline" (e.g., linear, Euler, and Navier-Stokes aerodynamics).
\marginpar{Many modern simulation projects could benefit greatly from
the use of integrated codes such as the earth system models.}
Issues concerning the internals of individual discipline modules were
not considered germane to the group.

\subsection{Status}
Various applications were represented in the working group, including
aeronautics,
geophysics (environmental and global climate modeling),
biological systems, and
plasma physics.
Chemistry, materials, astrophysics, and quantum chromodynamics
were not represented.

There was a surprising commonality among the different fields in terms
of both status and obstacles to progress.  In both aeronautics and
geophysics, integrated codes have been operational on Cray
supercomputers for several years.  Typically, these are limited to
relatively simplified linkages among a few components, with a strong
fluids component.  Codes have also been developed in heterogeneous,
networked environments.  Examples of such integrated codes include the following:
\begin{itemize}
\item
Simulation of aircraft, both a high-performance aircraft and
     a high-speed civil transport together with their propulsion
     systems, incorporating fluid dynamics, structural mechanics,
     surface heating, and controls models.
\item
Environmental models incorporating atmospheric dynamics, surface
     water, and groundwater models.
\item
Earth system models incorporating atmospheric dynamics, atmospheric
     chemistry, biosphere, and ocean dynamics models.
\item
Predictive computational models of cellular organelles, coupling
     fluid dynamics, rigid body mechanics, molecular dynamics models, etc.
\end{itemize}

In all these areas, there is strong interest in developing
increasingly sophisticated systems that couple ever more advanced
simulations of more diverse physical systems.  However, progress is
limited by two factors:
\marginpar{A common data model would capture data from individual
disciplines in a unified framework.}
computational limitations of current
supercomputers, and limited understanding of the physics and numerics
of the interfaces between components, especially with respect to
stability.  MPP computers are seen as a potential solution to the
first problem, but progress in this area will necessarily increase the
pressure on applications scientists to improve understanding of
coupling issues.

Multidisciplinary applications on MPP computers are for the most part
underdeveloped, primarily because of the difficulties inherent in
adapting not just one but a suite of existing codes for efficient
parallel execution.  This is a severe problem.  Indeed, many of the
group members felt that this may well be {\it the} pacing item in
demonstrations of HPCCP technology for real-world Grand Challenge
problems.


\subsection{Targets of Opportunity}

The consensus was that opportunities exist for enhanced
activities in four areas: common data models and database systems,
languages and tools for developing multidisciplinary applications, application design
optimization, and education.

\paragraph{Data Models and Database Systems.}
Data management is very much an enabling technology for
multidisciplinary design  and analysis. 
The current state of the art
involves a series of custom-designed data ``translators" that map
information between various applications.  These are expensive to
generate and require extensive validation.  The opportunity for errors
entering the multidisciplinary design process are greatly amplified across each application
interface.

A preferred data management approach would involve a ``common data
model" representation.  This common model would capture individual
discipline data fields within a unified representation that provides
consistent information at the boundaries of each discipline.
Disciplinary data is ``owned" by each discipline and available as
limited subsets of the unified representation.  This information can
be distributed across a variety of computing platforms.  Data access
requirements, as determined by the ``physics" of discipline interfaces,
define data protocols.  Where it is appropriate, object-oriented data
hierarchies are used to organize and minimize data storage while
providing linkages into discipline applications.  This approach is facilitated
by the use of geometry modeling standards and data standards.  For
example, in aeronautics, the Non-Uniform Rational B-Splines standard
(as embodied in the imminent NASA-IGES formal standard) enables
linkages between CAD systems, grid generators, visualization tools,
and application codes.  Finally, the database manager must organize
information across a variety of hardware platforms while maintaining
``virtual" ownership on perhaps a single workstation.  Efficient access
to a heterogeneous mixture of computing platforms (MPP to workstation)
is needed.

In summary, key points in this area are 
\begin{itemize}
\item
use of a common data model among disciplines,
\item
use of an object-oriented data hierarchy to organize data,
\item
standard geometry and data representations, and
\item
platform independence.
\end{itemize}

\paragraph{Languages and Tools.}
Better languages and tools are urgently required to support
development of multidisciplinary applications.  Currently, the vast majority of
component programs are written in Fortran 77.  Coupling is achieved in
a variety of ad hoc ways, including (a) creation of a single executable,
(b) files, (c) specialized shared-memory database routines, (d)
message-passing in a network, (e) AVS, and (f) intercube communication
on a hypercube.  In the future, an increasing number of components
will be written in C, C++, and Fortran 90.

There was a strong consensus within the working group that languages
were urgently required that addressed issues of encapsulation,
modularity, and portability in MPP and network environments.  Current
message-passing systems are seen as too clumsy, and either specialized
languages or graphical programming systems are required. 
\marginpar{Task-parallel and data-parallel modules should be integrated.}
The need to
support integration of both task- and data-parallel modules was
emphasized, as was the need for tools that permit MPPs to be
partitioned among disciplines, with simple and efficient communication
between partitions.

There was considerable debate within the working group as to the
importance of object-oriented programming techniques for multidisciplinary applications.  Several
argued that the abstraction capabilities of C++ were an important
technology that could be used to structure complex multidisciplinary applications codes and
encourage code reuse; others disagreed.  One member argued for
object-oriented extensions to Fortran 90 that supported inheritance,
by means of a preprocessor.

Two major topics of discussion were language interoperability and
standards.  For example, it is increasingly common for software
developers to use C++ wrappers to call Fortran 77 or Fortran 90
routines.  A standard procedural C++/Fortran interface would be very
helpful.  The reverse interface---from Fortran 90 to C++---would
also be useful for database access and graphics libraries.  Standard
message-passing systems were also seen as important.

In summary, key points in this area are the need for
\begin{itemize}
\item
languages and tools for systems integration aspects of multidisciplinary applications
     on MPP and networked computers,
\item
languages and tools for integrating task and data parallelism, and
\item
standard interfaces between different languages.
\end{itemize}


\paragraph{Applications Design Optimization.}
Multidisciplinary applications in both Grand Challenge projects and
industry possess significant complexity.
\marginpar{Input from industry is needed about ways to improve design
optimization methodologies.}
They may involve hundreds or
thousands of design variables and tens or hundreds of modules.  In
manufacturing, the move towards concurrent engineering introduces many
other considerations, since manufacturing or marketing considerations may
not be describable by continuum models.

A strong need exists for improved optimization methodologies and
software support for such complex problems.  The working group did not
feel that its members represented a sufficiently wide range of
expertise to make detailed suggestions in this area.  Input from
industry, universities, and government labs is urgently required to
define goals in this area, and this input should be taken into account
in defining HPCC program directions.


\subsection{Relationships}

Communication difficulties, both between applications scientists and
computer scientists and between application scientists working in
different disciplines, were seen as hindering progress in
multidisciplinary applications.
\marginpar{Multidisciplinary approaches are likely to become dominant
in more fields of science and engineering.}
In addition, several speakers alluded to lack of experience in modern
software engineering techniques among scientists developing components
of multidisciplinary models.  It was also felt that new graduates,
while often more exposed to new techniques, lack experience in
multidisciplinary research and industrial software engineering
problems.  Several solutions were discussed, including (a) an
increased emphasis on multidisciplinary degree programs (Georgia Tech
and Virginia Polytechnic Institute were cited as examples of successful programs), (b) improving
training in software engineering for science majors, and (c) programs
that involve students in software development projects at labs and
companies.

The working group expressed concern about the apparent lack of
interest among computer scientists in the problems facing developers
of multidisciplinary applications.  The current focus on getting single-discipline modules
running on MPP computers is understandable and justified.  However, multidisciplinary
approaches are likely to become dominant in most fields of science and
engineering, and better tools are urgently required.  This situation in turn
requires increased dialogue between computer scientists and
applications developers.  The working group emphasizes that good tools
will be developed only in a multidisciplinary technical environment,
in which groups having highly diverse combinations of computer
scientists, applications developers, etc., tackle the needed research
in this area.

The working group was also concerned with the difficulties facing multidisciplinary applications
developers who wish to learn about and evaluate new languages and
tools.  Currently, information is primarily disseminated by word of
mouth; better mechanisms are urgently needed.
\marginpar{Templates are the key to reuse of parallel code.} 
One approach would be
to prepare a set of ``template" codes representative of multidisciplinary applications concerns
and  to encourage software tool developers to use these templates when
evaluating their systems.


\subsection{Impacts}

The working group agreed that it is too early to expect the activities
of the HPCC program to have had significant impact on science and
industry.  However, there was broad consensus that by allowing
simulation of entire systems rather than individual components, the
combination of high-performance MPP computers and improved techniques
for coupling different disciplines can be expected to enable
significant progress in many areas of science and engineering.  We
give just three examples of where such ``whole system" simulations are
planned; each is dependent on availability of the greatly increased
computational capabilities that are being developed in the HPCC
program.

\begin{itemize}
\item
The aerospace industry is implementing integrated
product/process development (IPPD).  A key element of IPPD is the
development of total system simulations that allow the substitution of
computational prototypes for physical prototypes.  This approach could
substantially reduce design cycle time and development cost, and
improve product quality.
\item
In the environmental sciences, integrated earth system models could
greatly improve the timeliness and scientific foundation of data,
motivating important environmental decisions such as mitigation
strategies for global change.
\item
In biophysics, models that integrate multiple levels of knowledge
about protein structure would allow new approaches to the problem of
predicting the three-dimensional shapes adopted by complex proteins.
\marginpar{The availability of high-quality numerical algorithms and
libraries can determine the pace of applications development.}
Solution of this protein-folding problem would have great
significance, both for science and for drug design and manufacture.
\end{itemize}

\subsection{Findings}

The working group reached consensus on the following suggestions,
which are given equal priority.

\begin{enumerate}
\item
Steps should be taken to strengthen connections between multidisciplinary applications and
database communities.  New research in this area should be initiated
if necessary technology is not  already available.

\item
The HPCC program should encourage both research on languages and tools for
the systems integration aspects of multidisciplinary applications, and the timely dissemination
of the results of this research to the multidisciplinary applications community.  In particular,
\begin{itemize}
\item
   languages and tools for coordinating execution of sequential,
     parallel, and data-parallel components on MPP and networked computers
     should be made a research priority;
\item
   a framework should be established for the evaluation of such tools
     in multidisciplinary applications; and
\item
standard interfaces should be defined between programming languages
     commonly used for development of multidisciplinary components (e.g., Fortran 90
     and C++).

\end{itemize}
\item
Industry and university input should be sought to define HPCC program
directions in applications design optimization.

\item
A program should be established for retraining current scientists
and engineers in critical HPCC multidisciplinary technologies, and the university
community should be encouraged to establish or strengthen
multidisciplinary programs and curricula.

\end{enumerate}
In addition, several group members suggested that researchers
working on load-balancing techniques, visualization tools, and
parallel debuggers be encouraged to consider the particular
requirements of multidisciplinary applications.


\section{Group F -- Algorithms and Libraries I}

\noindent
{\bf Chair:} Ahmed Sameh; {\bf Co-chair:} Phillip Colella

The lack of high-quality numerical and symbolic algorithms and libraries
often limits the pace of applications development. This situation is particularly true 
on massively parallel machines. Group F considered the state of the art
in algorithms, libraries, and software for the numerical solution of partial
differential equations that arise in various applications, and the associated
linear algebra algorithms and kernels.
\marginpar{Flexible parallel languages will allow users to match their
algorithms to a specific architecture.} 
The majority of the members of this break-out session were application scientists
who are not only experts in their applications domains but also seasoned
computational scientists quite familiar with computer architecture and system
software, as well as with the design and implementation of numerical and nonnumerical
algorithms. Furthermore, almost all of them are associated with interdisciplinary
activities that involve computer scientists and applied mathematicians. In spite
of this, our deliberations considered the needs of applications scientists who do
not have the benefit of such expertise or the benefit of the
involvement of the computer science and the applied mathematics communities.

%Participants in this break-out session are:
%%%%
%1- Louis Auslander            10- John Gardner
%2- Alfred Brenner             11- Melvyn Goldstein
%3- Daewon Byun                12- Adolfy Hoisie
%4- David Cargo                13- Andrea Malagoli
%5- Charles Clark              14- Ahmed Sameh (Chair)
%%6- Phillip Colella (Co-Chair) 15- Kamy Sepehrnoori
%7- Anil Deane                 16- Richard Sincovec
%8- Richard Draper             17- Paul Swarztrauber
%9- Richard Ewing              18- Mary Wheeler

\subsection{Status}
The working group focused on three areas:
research activities, software-sharing mechanisms, and federal support.
 
\paragraph{Major Research Activities.}
Grand Challenge teams assembled at national laboratories, universities,
and a few industries represent a small number of computational scientists
who are sophisticated users of advanced computer architectures and who are
willing to work hard at implementations that result in high performance.
While the HPCC program is not limited to parallel computing, the design of
parallel algorithms for a variety of applications is a vital component of
the program.

For some application domains, parallel algorithms can be
provided by generic mathematical software libraries that are finely tuned for
a few architectures and that contain some of the basic algorithms for FFTs,
linear algebra kernels, and algorithms for dense and sparse matrix
computations.
While such libraries may be sufficient for some applications
scientists, however, however, they are far from being satisfactory for sophisticated users or
for users seeking high performance. Factors that limit the usability of
such generic libraries include limited functionality, inappropriate data
structures, and lack of algorithm scalability. This situation shows the need for parallel
languages that allow sophisticated users to match their algorithms to the 
architecture at hand, namely, by selecting appropriate data structures and by
matching data movements to architectures. For less sophisticated users, it is
advantageous to incorporate such features in the compiler as well. In addition 
to the need for parallel languages with the above flexibility, the group pointed
out the need for (i) application-specific libraries that may include mesh
generation tools, discretization tools, solution and update tools, and mesh
refinement and load-rebalancing tools, and (ii) application-specific languages
such as FIDIL for computational fluid dynamics.
Applications scientists who are reluctant or unable to make the investment required
to create large-scale applications codes that achieve the highest performance
possible for each new architecture that comes along must have available generic or
application-specific libraries of value for rapidly producing prototype codes
that test or verify computational models. This rapid prototyping may then be
followed by incremental refinement to enhance performance for a 
particular architecture. Such refinements should entail
further algorithm/architecture matching, including parallel communication
utilities for changing data structures and implementing other common communication
tasks.

\paragraph{Software-Sharing Mechanisms.}
Software sharing occurs mainly between cooperating groups. Application
scientists are reluctant to exchange software for two major reasons:
\begin{itemize}
\item
The software is experimental and often poorly documented.
If such software is not used properly by others, it may reflect badly
on the investigator.
\item
Distribution and maintenance of large-scale application codes is a
very expensive activity. Researchers at universities and national laboratories
are not funded explicitly for providing high-quality sharable software
that can be used by others or for providing consultation on its use.
\end{itemize}

One approach is to fund activities
at some institutions where application scientists are working closely enough
with systems software groups that are capable of supporting and maintaining
the resulting software for a much wider community. Software that has
a large enough market should be transferred to an interested industrial
partner.
\marginpar{Software with a large market should be transferred to an
industrial partner.}

Sharing mechanisms may not be limited only to complete software packages;
templates (or pseudo-code representations) of general algorithms that are
language- and architecture-independent may be of great value as well.

\paragraph{Federal Support.}
The federal HPCC program has concentrated mainly on Grand Challenge problems
and teams to solve them. Concern has been expressed that support for the
underlying software infrastructure may be inadequate. By software infrastructure,
the group means (i) system software for MOP's (i.e., languages, compilers, and
communication primitives); (ii) research in new application-specific algorithms
and the resulting software on a variety of architectures; and (iii)
application-specific languages. Also, as mentioned above, additional funding is needed for
supporting the maintenance, distribution, and consulting activities for 
applications software of value
to a large group of users in the national laboratories and universities. Industry
involvement is highly desirable in the distribution of higher-quality versions
of such software.

\subsection{Targets of  Opportunity}

Several areas of research need to be strengthened, while others need to
be initiated:
\begin{itemize}
\item
Algorithm and software developments in application-specific domains,
    including parallel computation and communication algorithms.
\item
System software that allows easier and more efficient use of
    parallel architectures. Such software should include
    language notation for data mapping, good compilers, and effective
    debugging tools.
\marginpar{Computer vendors can help interdisciplinary teams.} 
\item
Application-specific languages that increase the level of abstraction
    sufficiently so as to respond to architectural changes quickly and,
    if this language can be compiled efficiently, without loss of 
    performance.

\end{itemize}
\subsection{Relationships}

The group believes that the HPCC initiative deserves credit
in fostering interaction between the computer science community and the
applications scientists. 
While most members of this break-out session belong
to both communities, roughly half expressed the need for more cooperation
between these two communities in their own institutions. The HPCC program 
has started to change the culture in several science and engineering disciplines
by demonstrating that computation is just as important as theory and experiments
in advancing these fields.

The most important barrier to interaction between computer and applications
scientists is the cost of entry. Most computer scientists have no background
in any of the classical science and engineering disciplines, and the majority
of engineers and scientists are not aware of the most advanced parallel
computer architectures or state-of-the-art algorithms for these computers.
In fact, it has been demonstrated that application experts can more successfully
bridge this gap than computer scientists. One approach is to prepare graduate
students in the interdisciplinary field of computational science and engineering,
which is the interface between computer science and the classical disciplines
of science and engineering. Resistance to establishing such new programs in
various universities may be overcome by federal funding of
the most promising programs. Also, computational scientists (who are amply
represented in the Grand Challenge teams at national laboratories) need to gain
the confidence of their experimental colleagues by seeking experimental validation
of their computational results.  

Interaction with industry (including computer manufacturers) is vital to the
goals of the HPCC initiative. It is through industrial interaction that the 
knowledge from the Grand Challenge teams can be disseminated.
\marginpar{Staff exchange programs should be endorsed.}
Members
of the group also
pointed that computer manufacturers can be of help to interdisciplinary teams involved in the research activities outlined above by providing
access to dedicated machines and assistance in implementing Grand Challenge
software on their machines. Access to dedicated machines is essential, as
the development of finely tuned algorithms and whole applications codes
require performance-monitoring and measurement tools that are intrusive
in multiuser environments. Expansion of existing approaches, or
development of
new ones, for working with industry is vital if the HPCC program is to have
an impact on industrial competitiveness. With computer manufacturers, these
approaches could facilitate more effective computer architectures and systems
software, as well as the development of applications software and underlying
software libraries on new machines. 
Collaborations with other industries will
facilitate technology transfer to those industries that can substantially benefit
from the technology produced in the HPCC program. Such technology transfer can
be significantly enhanced through exchange of researchers among universities,
national laboratories, computer manufacturers, and industry, in general.

\subsection{Impacts}

The group identified several ways in which the HPCC program has had a major impact on algorithm and library
development:
\begin{itemize}
\item
The HPCC program has provided a mechanism for funding state-of-the-art
    applications software. Industry involvement in this program needs to be
    strengthened.
\item
The program has dramatically increased the number of scientists and 
    computational scientists with hands-on experience with parallel computers.
    This represents an advantage to U.S. research and development similar
    to that provided by early experience with compilers and operating systems
    several decades ago. It is imperative, however, that continued and 
    increased support be provided for educational programs in computational
    science and engineering so as to ensure a permanent supply of researchers
   who combine both application and computer science expertise.
\item
Parallel algorithms have been beneficial to most of the Grand Challenge
    applications represented in this session. In fact, several application
    scientists observed that developing parallel algorithms for their applications
    often leads to faster execution on uniprocessors. Since massively parallel
    systems are not mature, however, more time and resources are needed before the
    impact of parallel algorithms on applications is fully realized on such
    architectures.
\item
Grand Challenge problems have led to increased understanding and use of
    broad-band communication structures and large distributed databases.
\end{itemize}

\subsection{Findings}
With respect to technical issues, we suggest that 
application-specific systems software and libraries be developed so as to
   provide a stable platform on which Grand Challenge applications can
   be built.
\marginpar{Applications-specific libraries must provide a stable
platform for Grand Challenge applications.}
 Such platforms should address
\begin{itemize}
\item
the development and implementation of algorithms in support of
         applications, including parallel communication algorithms;
\item
high-level language support for data mapping and data communication; and
\item
increasing programming productivity, for example, by
application-specific languages.
\end{itemize}

With respect to 
institutional issues, we make four suggestions:
\begin{itemize}
\item
enhance and expand technology transfer through scientists exchange and 
         extended collaborative efforts on specific end-user applications;
\item
promote interagency and intermission activities in application-specific
            algorithms (e.g., PDE algorithms across several disciplines);
\item
redress the HPCC program imbalance: specifically, fund manufacturer, applications,
         and systems collaborations for developing software
infrastructure; and
\item
support educational programs in computational science and
         engineering or in scientific computing.

\end{itemize}


\section{Group G -- Algorithms and Libraries II}

\noindent
{\bf Chair:} Fran Sullivan; {\bf Co-chair:} Bob Sugar

%sugar@sarek.physics.ucsb.edu

%From fran@super.org Wed May 26 06:58:39 1993

The development and implementation of algorithms for massively
parallel computers are important components of the HPCC program
and should receive greater emphasis than they currently do.
This is an area in which collaborations between computer scientists
and applications scientists  can yield major dividends.
Such dividends have already 
begun to be realized within the Grand Challenge 
applications groups. As indicated below, however, we believe that there
are a variety of problems on which new collaborative efforts are needed. New
approaches  are also needed for sharing algorithmic ideas and software.
In addition,
vendors must pay greater attention 
to the needs of the applications teams if the
use of massively parallel machines is to progress.

\subsection{Status} 
Grand Challenge applications groups are developing algorithms
for specific machines and writing optimized code to implement them.
A number of groups are working on closely related problems
and might benefit from greater communication.
\marginpar{Modern simulations often require new algorithms that have
not been extensively used in scientific computing.}

Existing software-sharing mechanisms are not  working well
and not rapidly enough to be of real use to the individual Grand
Challenge participants.  There was a strong consensus that
conventional libraries were not at all appropriate to the
rapidly evolving needs of the Grand Challenge community.
Codes are developed by the teams themselves, and the teams
cannot find time to produce well-documented,
distribution-quality  programs.  
The usual vendors such as IMSL cannot contemplate entry into
a very thin market with fast-evolving algorithms. The strong
emphasis on code optimization for individual
machines makes portable libraries impractical at this time.

\subsection{Targets of Opportunity}
In the context of the HPCC program and its associated Grand
Challenge applications, the following specific areas of research 
were identified:
\begin{itemize}
\item
Algorithms for decomposition of regular and irregular data      
structures arising in PDE discretization to obtain efficient load 
balancing, and fast algorithms for solving elliptic PDE on these types 
of structures.  Of particular need are efficient 
methods that can be used with adaptive methods where these 
types of load-balancing issues must be performed dynamically 
as the discretization adapts to the solution structure.
\item
Parallel random number generators with appropriate speed 
and correlation properties. Correlation of random numbers 
across processors has also been a major source of concern.
Software to test these programs is also needed.   Randomized 
algorithms are a major technique in algorithms research and 
often are the only effective way to overcome NP-hard
problems.  For such algorithms, the availability of parallel
random number generators is fundamental.
\item
Symbolic and combinatorial (as opposed to floating
point) algorithms. Symbolic algorithms are currently useful in a variety of
local computations within Grand Challenge problems.
We expect combinatorial algorithms,
numeric (but not necessarily floating-point) computations,
and other symbolic methods to
gain in importance in such fields as biotechnology in the
coming years.
\item
New algorithms for irregular problems. Sparse systems and 
irregular patterns of data are not being addressed by the
algorithms that are currently available. In order for these
systems to be useful, good parallel
algorithms are needed to handle these structures.
\item
General multidimensional pattern recognition and pattern-matching algorithms. These will provide informational      
structures to analyze informational structures in higher-dimensional spaces,
which are useful,   for example, in three-dimensional    
molecular structure databases.
\end{itemize}

Robust, correct implementations of these algorithms would be 
extremely useful and, on the whole, are not available. However,
work on the algorithms themselves is as important as work on the
implementations. 

The HPCC should provide strong encouragement and incentives
to vendors to develop robust software environments for their MPP
hardware (concurrent with hardware delivery). Lack of this support
has significantly hampered algorithm development and implementation
on these machines.

\subsection{Relationships}

Although most researchers have the feeling that
the Grand Challenge
community (GCC) is not reinventing techniques already known to the
computer science community (CSC),
the true situation is the opposite:
the GCC is, in fact,
duplicating work several times over. 
\marginpar{It is imperative that the Grand Challenge community not
reinvent computer science techniques.}
Two factors lead to this situation.
First,
applications scientists believe that
they require specialized algorithms for each
application, so that general methods cannot be used.
Second, 
and more distressing,
large segments of the CSC have decided that their discipline
can and should be isolated from the messy, real-world problems of
Grand Challenges.
While the GCC is making an effort to learn what is already known,
parochial issues
from both sides hinder
such efforts.
The recent NRC report ``Computing the Future''
strongly suggests that computer science should, to a much greater
extent than is now the case, derive inspiration from real-world
applications such as  the Grand Challenges.  We repeat that suggestion 
and strongly support the collaborative interdisciplinary requirements
of the HPCC Grand Challenge program.  These have the salutary effects
of allowing the GCC to become aware of the work of computer scientists
and of bringing that additional talent in computation to bear on
Grand Challenge problems.

We note with caution that there are significant problems in the
universities for junior faculty in computer science
who choose to work on applications. 
It is not always possible for them to be rewarded for doing
``computer science" for such work.  Part of this is due to the
departmental structure of the universities---a problem not
generally shared by the laboratories.  Part is due to
a tendency of algorithmists in computer science to work on theoretically
elegant rather than real-world problems.  
We approve of current efforts to remediate the problem.
There have been several multidisciplinary
programs recently begun in computational science at several
universities, and there is a small but growing fraction of the
CSC working with Grand Challenge-like problems.
We encourage increased
interactions
between the national and industrial labs and the faculty  and
graduate students in computer science. Another useful activity would be
to organize open research conferences on topics related to
Grand Challenge problems.

\subsection{Impacts}

A short-term impact of the HPCC program has been in providing massively parallel
computers  to Grand Challenge research teams.  This has had the
effect of promoting parallel computing  as an industry. In addition,
the Grand Challenge teams are quite sophisticated  and have been
able to affect the usability of these computer systems.
By striving for performance on immature systems, they have uncovered
software and hardware inadequacies in these systems, and 
vendors have used this information to improve their systems.  Thus
the research teams benefit the entire community by informing the
vendors
of mistakes and pitfalls
at early stages of the development cycle.
\marginpar{HPCC research provides valuable feedback to vendors early
in the design cycle.}
One hopes that the teams will also encourage the vendors  to provide a
stable and understandable set of development and optimization tools. 
Such tools, in turn, would smooth the way for the rest of the scientific
computing community as they try to enter the massively parallel
computing field. 

Another important impact has been on high-technology industries.  
It is interesting to note that workstation vendors are designing
multiprocessor machines.  IBM, Sun, HP,  and SGI have all started
to provide high-end multiprocessor workstations.  In addition,
some other industries have begun to use parallel computing in
their own research and development.  These include the oil industry,
where multiprocessors are used for exploration and analysis of seismic data;
the aerospace industry, in collaboration with NASA, for the design
of new aircraft; and the materials industry, in collaboration
with DOE, for the computational analysis of materials properties. 
It is hoped that the continued success of these efforts will lead
to the desired long-term impact of HPCC: improved industrial
competitiveness.  

Another objective of HPCC is to foster collaboration between the
computer and application science communities.  To date, increased
collaboration has occurred among members of Grand Challenge teams,
but the impact of HPCC directly on computer science has been small.
\marginpar{The trend is clearly toward more realistic models of computation.} 
There does seem to be a trend in computer science towards the
experimental, and the  theoretical community is considering more
realistic models of computation than the PRAM.
Nevertheless, the computer science
community is still perceived as too theoretical for the immediate
needs of HPCC. 

Finally, while most of the participants believe that MPPs are the future trend,
they noted that
some short-term goals of the Grand Challenge
projects still require the more mature technology of vector machines.
Some researchers
feel that  it is too soon to assume that the continued health
of the computer industry will come only from massive parallelism. 

\subsection{Findings}

To facilitate work on software development, vendors should
\begin{enumerate}
\item
balance  memory bandwidth with CPU speed;
\item
balance communication speed with CPU speed;
\item
provide good node compilers to allow programmers to make more effective use of machines without resorting to assembly language coding;
\item
provide fast multiple-precision arithmetic
(the predominant techniques are still numeric and greater size of     
problems demands greater precision. It should be noted that some
participants disagreed on the importance of this suggestion, while
others find multiple-precision arithmetic essential to doing serious work on these machines);
\item
pay greater attention to symbolic and combinatorial aspects of
computing, such as fixed-point arithmetic;
\item
provide language constructs that allow the easy definition
of complex data structures (C++ is an example of a high level
language that allows this);
\item
provide high-level standardized message passing routines; and
\item
provide a random number generator with their machines.
\end{enumerate}

The problem of sharing of information among scientists is very 
important. However, we do not think that the massively parallel
field is mature enough yet to warrant portable standardized
libraries.
\marginpar{Informal libraries should be set up at the national
supercomputer centers.}
 Portable libraries would not be efficient enough to be
used widely and so are not reasonable at the moment. However,
we strongly suggest establishing some system of informal
libraries with a different set for each machine class. These
informal libraries would include algorithms, templates,
and code. The library servers 
could be set up at different servers in different
places (perhaps for different machines).  The national
supercomputer centers would be logical choices for locating
such servers.

Finally, we suggest that the
current practice of requiring computer scientists on Grand Challenge 
teams
be continued.
This practice
has stimulated interactions between
these groups.
Efforts are also needed to
increase interactions among scientists in the academic, laboratory,
and industrial communities. Although the HPCC has helped to
bring the first two closer together, greater efforts are needed
to increase the participation of the industrial community. This
is essential if the program is to succeed in its important goal
of improving U.S. competitiveness.


\newpage
\section{Group H -- Graphics and Visualization}

\noindent
{\bf Chair:} Paul Woodward; {\bf Co-chair:} Tom DeFanti

\subsection{Status}

The scientific visualization working group reviewed the current status
of software and hardware development activities designed to support
the high-performance computing community.
\marginpar{Graphics and other visualizations are crucial to modern
scientific applications.}
One of the principal
trends in this area is the development of specialized systems and
environments for the interactive exploration of very large datasets.
Grand Challenge computations are often characterized by either the
use of or the generation of datasets of a terabyte or more.  These
datasets are so large that visual representations are often the only
means available to examine the data.  Even with image representations,
the datasets are still so large that many views must be generated.
Several groups are engaged in developing tools that enable a
researcher to explore such vast datasets, bringing into the computer
or workstation memory only small subsets of the data, which are
specified interactively by the researcher, and then rendering them
in any of a number of ways in real time.  Developing such data
exploration hardware and software systems places extreme demands
upon the computer network, I/O subsystems, image-rendering engines,
and even the display subsystem if the data is to be displayed
at full resolution.

The working group noted efforts at several centers to develop
{\it image-rendering software} that can be executed on massively parallel
processing systems.  The need for this software is driven by
the immense size of the datasets to be visualized.  Often,
the dataset can be mounted and dealt with only on the MPP
system that created it.  Even when other systems can access the
dataset over the network, rendering of images on the MPP may be
required if only this system has sufficient rendering speed to produce
an image of the data in a reasonable time.

The development of software to support scientific visualization in
{\it distributed, heterogeneous computing environments} is proceeding
rapidly.  This activity is vital if visualization requirements of
researchers remote from the Grand Challenge computing hardware are
to be met.  However, the working group noted that much of this
effort is currently aimed at visualization of significantly much smaller
datasets than are common in the Grand Challenge applications
community.  Special efforts of this sort targeted at support for
Grand Challenge applications are needed in the future.

The working group also discussed research activities in developing
{\it virtual reality} environments in relation to the Grand Challenge
applications.  Both immersive displays and large flat-screen
displays were discussed. 
\marginpar{Visualization can produce comprehensible views of real
systems such as automobiles and airplanes.}
 The principal advantage of these efforts
for Grand Challenge applications was agreed to be their potential
help in dealing with the visualizations of extremely complex systems.
Both approaches offer high-resolution displays that can, if the
accompanying I/O and data-rendering systems are large and fast
enough, put onto a single display much more than the small window
of the workstation screen to which researchers have become accustomed.
In either case a familiar mechanism is supported for navigating
through the dataset, that of walking for the immersive systems,
and that of driving (or flying) for the large-screen systems.

A brief discussion of fundamental research in visualization techniques
noted that the NSF Science and Technology Center for Computer
Graphics and Scientific Visualization is developing new rendering
techniques that are more soundly based upon the physics of {\it radiative
transfer and scattering.}  These new techniques should be capable of
producing much more photorealistic images of real systems, such as
automobiles and airplanes.  The usefulness of these techniques in
enhancing the perception of physical systems that cannot normally
be seen by the eye, such as complex molecules or the structure of
the earth's magnetosphere, remains to be seen.  Presumably, success will
depend upon the extent to which familiar visual cues, such as surface
textures, can be employed to convey significant scientific data in
these unfamiliar settings.

Use of scientific visualization for the {\it remote steering} of instruments
such as microscopes and telescopes was also noted as an important
current activity.  Remote steering of supercomputer simulations,
although possible using visualization techniques to develop the user
interface, is not yet common in this community because of the enormous
amount of computer time required for a single Grand Challenge run.

Finally, the working group noted the tremendous usefulness of scientific
visualization in {\it education, training, and technology transfer.}  Not
only are visualizations of complex calculations indispensable in
explaining the science involved to nonexperts, but also visual user
interfaces to the associated simulation software serve to reduce
barriers to use of the software by nonexperts.

\paragraph{Software-Sharing Mechanisms.}
Present software-sharing mechanisms consist of FTP servers at various
centers and of informal software sharing among collaborators.  These
mechanisms are effective but inadequate.  Clearly, some
mechanism is needed for locating software that is available over the network.
In addition, more incentives to share software are needed.
There is a reluctance to share software among competing research groups
and centers, which is in part a result of the system that sets these
groups up as competitors rather than as collaborators.  Direct and
visible rewards for the sharing of software could change this
situation.  This problem is not universal in the community, but it
needs to be addressed by the funding agencies, who are the only members
of the community with a means to change the present system of incentives.

\paragraph{Balance of HPCC Funding.}
The working group concluded that present HPCC funding is too strongly
skewed toward the purchase of MPP hardware and that more support is
needed for the disk and I/O subsystems, networks, software tool
development, and people who are required to make these MPP systems
useful in addressing Grand Challenge applications.
\marginpar{Innovative graphics include volume rendering and virtual reality.}
 As developments
in compilers and application programs allow us to extract more and
more of the potential performance from these MPP systems, this support
issue becomes increasingly critical.


\subsection{Targets of Opportunity}

The primary system needs of HPCC scientific visualization that
computer system vendors should address are as follows:
\begin{itemize}
\item
higher I/O bandwidth,
\item
effective interactive operating systems,
\item
high-speed network interfaces, and
\item
better compiler and debugging tools.
\end{itemize}

The working group identified several areas that need enhanced federal funding support:
\begin{itemize}
\item
development of scalable parallel visualization algorithms,
\item
tools and environments for interactive exploration of massive datasets,
\item
development of new visualization/analysis paradigms,
\item
graduate student support for work in this area, and
\item
conferences and workshops that bring this interdisciplinary
community together.
\end{itemize}

\subsection{Relationships}

The working group felt that not enough computer science
research in scientific visualization is addressing Grand Challenge
applications.  These applications require visualization tools with
\begin{itemize}
\item
embedded quantitative analysis capabilities, such as the generation of
individual numbers, various statistical measures of the data, spectra,
and other operations that may be application specific;
\item
support for complex data types and structures, such as those that
arise from overlapping, adaptively refined, or unstructured grids,
staggered grids, and binary tree structures;
\item
high efficiency for use with large datasets.
\end{itemize}

To improve collaborations between computational and computer
scientists in this area, the working group felt that programs aimed at
interdisciplinary collaborations and workshops could be enhanced.
\marginpar{An easy-to-use, interactive tool to guide large-scale
computations at run time will dramatically improve scientists'
productivity.}
These collaborations would also benefit greatly from better network
connectivity and associated network-based collaborative software
tools.

\subsection{Impacts }

The impact of scientific visualization upon the HPCC program is chiefly
based upon the role of visualization as an enabling technology, which
allows a researcher to understand extremely complex physical systems.
Not only does visualization enable the primary researcher's understanding
of his simulation data, but it also enables him to communicate that
understanding to other members of an interdisciplinary community of
experts, and, equally important, to the public that ultimately is
called upon to support the research.
Secondary impacts of scientific visualization on the HPCC program
include its role in driving requirements for network bandwidth and
for the interactive use of MPP computer systems.

The impact of the HPCC program on scientific visualization research
is chiefly to drive the development of scalable visualization
algorithms.  Related to this is the development of multidisciplinary
tools for the visualization of massive datasets.

The impact of all these developments on industry is to reduce the
barriers to the use of HPCC application codes in industry.  Graphical
user interfaces are perhaps the most effective means of promoting the
use of new and complex software packages by scientists not trained in
the details of the inner workings of those codes.  A common difference
between commercial and research code packages is the lack of flexible
and powerful graphical user interfaces in the latter codes, for
handling both data fed into a simulation model and data produced as code
output.  With accelerated development of modular visualization
software targeted at Grand Challenge applications, the development of
such user interfaces will represent a much smaller fraction of the
overall code development effort and therefore make technology transfer
to industry both easier and more common.

\subsection{Findings}


The visualization working group
focused on the following findings:
\begin{itemize}
\item
{\bf Implementation of
visualization techniques on massively parallel computers.}
Most Grand Challenge computations will
be performed on powerful parallel computing platforms.
In many cases, only these systems will have enough memory, computing
power, and peripheral storage to visualize the simulation data.
Therefore, visualization algorithms must be developed
that run efficiently on such systems.  These visualization
programs must be able to run in a highly interactive mode, in order to
permit researchers to explore datasets dynamically.  Visualization
techniques of particular interest to the Grand Challenge applications
community for implementation on MPP systems include three-dimensional perspective
volume rendering, polygonal surface rendering, and combinations of
both these techniques.  The tremendous computing power offered
by MPP systems will also enable more sophisticated, physically
based rendering methods that can capture more of the information
inherent in the simulation.  Such advanced rendering techniques
might include, for example, a detailed treatment of light scattering
from textured surfaces.

\item
{\bf Very large datasets.}
Visualization packages like AVS and Explorer,
which allow many groups to write their own specialized modules and
then to incorporate them into a larger, unified whole, are an excellent
means of software sharing and reuse.  However, a number of flaws in
such packages were noted that relate to the essential Grand
Challenge need to visualize very large datasets. 
\marginpar{Many Grand Challenge-type applications are too enormous to
be viewed in real time.}
If visualization
software sharing and reuse are to become the rule in the HPCC community,
research is needed on modular, high-performance visualization environments
that are scalable to handle very large Grand Challenge applications
and that are capable of utilizing powerful MPP systems.
These environments should allow quantitative information to be extracted
from a dataset using the visualization tools, and they should allow
a variety of application-specific analysis modules to be incorporated
into the visualization environment.  These environments should also
support a broad range of data types and structures.  In particular,
effective means of visualizing vector fields in three dimensions are needed,
as are mechanisms to handle data specified on overlapping or
unstructured meshes or on staggered or adaptively refined grids.
Many of the Grand Challenge projects involve interdisciplinary
collaborations, and the visualization environment must be able to
handle simultaneously the preferred data structures for each
discipline.

\item
{\bf Highly interactive
response.}
The Grand Challenge applications, because they often
involve computations on extremely fine grids, demand large data
volumes (and very high image resolution). The size of the
datasets from these applications can exceed a terabyte, making it
impossible to render all the data into any single image. 
Consequently,
researchers often wish to explore their datasets interactively,
with only a fraction of the data displayed at any single instant.
This drives a need to get as much data onto the display as possible
in each image, and hence very high resolution display is essential.
\marginpar{Complex computations require a highly interactive response.} 
In addition, the researcher needs an effective means of navigating
through his dataset, keeping at all times a sense of where he is
with respect to the whole.  Virtual reality systems offer one means
of creating a natural sense of this navigation through the data.
Flat-screen-based displays allow one to navigate through the data
as if in a virtual vehicle.  There are tradeoffs in these approaches
involving resolution and display speed, as well as other factors,
and research in this area is needed.  Any such approach, however,
has a fundamental need for very rapid image rendering and display
under interactive user control, and with a special emphasis on very
large datasets.  A high degree of parallelism in both the rendering
engine and in the I/O subsystem is likely to be a common feature
of these interactive visualization systems.

\item
{\bf Massive data storage, retrieval, and
management in a heterogeneous networking environment.}
These data
storage, I/O, and networking issues have been separated out for
detailed consideration by other working groups.  Nevertheless,
our summary of the needs of high-performance scientific visualization
could not be complete without stressing the fundamental dependence
of this activity upon the infrastructure, both hardware and software,
for data storage and for data movement from one device to another.

\item 
{\bf Increased support of
visualization infrastructure.}  This infrastructure
includes visualization hardware and software both at high-performance
computing centers and at the researcher's home institution,
both wide area and local networks, and support people both at
centers and working locally with researchers.  Development of this
visualization infrastructure for high-performance computing is a
natural follow-on to the nation's successful program of installation
of high-performance computing infrastructure.  The new HPCC computing
systems have unprecedented power to perform scientific computations,
but unless the capability to visualize the results of these
computations is adequately supported, many of the potential
scientific discoveries of this program may elude us.

\end{itemize}

%From catlett@ncsa.uiuc.edu Fri May 21 17:37:16 1993


\section{Group I -- National HPCC Infrastructure}

{\bf Chair:}  Charlie Catlett; {\bf Co-chair:} Maxine Brown
%Working group report
%C. Catlett, Chair
%M. Brown, Co-Chair

%DRAFT 5/21/93

%        Outline:
%                1. Defining "Infrastructure"
%                2. Technology Infrastructure
%                3. Human Infrastructure
%                4. Findings
The global economy is rapidly shifting toward information technologies.
Advanced information technologies (including supercomputers, new paradigms
for immersive data analysis, digital libraries, concurrent engineering, and
the ability of workers to use these technologies) form the basis for
economic competitiveness during the next fifty years.
The present U.S. infrastructure provides access to goods and services, natural resources,
manufacturing centers, and other components of an industrial society.  The
HPCC infrastructure must provide access to information technologies, goods,
and services that will be necessary in order to compete in the new,
information-based global economy.



\subsection{ Status}

The temptation exists to think of the HPCC infrastructure in terms of
capital investments such as supercomputers, databases, and networks.  These
are the most tangible components of the infrastructure; however, they are
only the building blocks from which an infrastructure can be constructed. 
\marginpar{The federal HPCC program requires coordinated efforts
between individual institutions and funding agencies.}
We define the national HPCC infrastructure in terms of two distinct but
interdependent areas:  technology infrastructure and human infrastructure.
The HPCC technology infrastructure consists of three layers: 
\begin{itemize}
\item
Hardware and networks, the bottom layer, includes computing hardware
(supercomputers, workstations), networks, and storage archives.
Here, we focus on systems that are available to the
national community, including national networks and computing and storage
systems available to the national community (as opposed to private
computing resources).
\item
Resources and services, the middle layer, 
include such resources as national HPCC centers (providing computing services)
and digital libraries (providing access to information)
as well as support services
for mobile and
wireless hosts and workstation-based video conferencing.  The resources
and services provide a bridge between data and information.
\item
The user environment, the top layer, includes
software systems that allow the
user to locate, access, and effectively utilize services and resources and
to collaborate with other users.  This is the environment in which the user
``navigates" through information and through the lower two layers of the
technology infrastructure.  The user environment is a
bridge between information and knowledge.
\end{itemize}
Each layer builds on the layer below
and provides infrastructure for the layer above.

Cutting across these
three layers is a human infrastructure component
consisting of teams of professionals, centers, and
various communities.  These human structures provide services and are
themselves resources in terms of consulting, training, and development and
advancement of the infrastructure itself.

\subsection{Targets of Opportunity}
The working group identified targets of opportunity for each of the layers outlined above.

\paragraph{ Technology Infrastructure: Hardware.}
The need for teraflops computing power is well established for Grand
Challenges.  Today a number of supercomputers are available that have
ideal peak processing power of over 100 Gflops.  The current set of large,
scalable machines will rapidly be outgrown by the current Grand Challenge
teams, and so the infrastructure of large-scale systems needs to continue
to expand both in terms of the number of moderately sized machines
available for training and code development and in terms of the size of the
major Grand Challenge systems.
\marginpar{Peak performance is not important---teraflops application
performance is.}
At least as important as the amount of memory are the
processor-processor and processor-memory I/O bandwidth and latency and the
external I/O bandwidth.
We stress that
teraflops ideal peak or even ``benchmark" rates are not important to Grand
Challenge applications---teraflops application performance is.

The Grand Challenge teams are projecting single computational experiments
that create $10^9$ to $10^ {12}$ bytes and archives (storing results from multiple
experiments) of $10^{13}$ to $10^{15}$ bytes for a single Grand Challenge team
archive.  With teraflops computers, these experiments will create $10^9$ to
$10^{12}$-byte data sets in a matter of hours.
Revolutionary approaches to data storage are essential.  Moreover,
data must be readily accessible.
Today's high-performance computing environments
can routinely handle datasets up to several gigabytes with effective transfer
rates at between 1 and 10 Mbytes/s.
A balanced environment, according to the Grand
Challenge team surveys, should allow single data files to be transmitted in
1 to 10 seconds and datasets (of a thousand such files)
in 15 minutes to 2 hours.  Coupling this with the data file and dataset
sizes above results in the need for effective data transmission rates of
$10^6$ to $10^9 $ bytes per second---the higher rate being ten times the peak
throughput of high-end local area networks in 1993 and twenty times the
highest observed throughput of the fastest tertiary storage devices
available in 1993. 

To meet these increasingly higher data rates
will require networks
that can support multiple gigabits per second.
In addition, the network capabilities requirements of both the high-end
and the average user community continue to grow.


\paragraph{Technology Infrastructure: Resources and Services}
Current
network-accessible resources and
services are divided into three classes:
those that are
experimental (MOSIS, WhitePages), those
supported as unique national facilities (supercomputer centers, NIC,
library search capabilities), and those supported by community and/or
volunteer efforts (on-line FTP archives, WAIS, World Wide Web, Gopher
servers, USENET news).
New services
are outlined below,
with an analysis of their projected impact on the infrastructure.
\begin{itemize}
\item
{\bf Information services}.  These are becoming increasingly richer, with digital
libraries, yellow-pages, white-pages, and data archives providing more
intuitive user interfaces and thus growing in popularity.
\marginpar{The volume of scientific data being generated is growing exponentially.}
\item
{\bf Data archives.}
The amount of data being generated is growing at an
exponential rate.  Instruments are becoming more commonplace on the
network, and remote control of these instruments as well as real-time
archiving of data will produce data streams between 10 and 100 MBytes/s. 
Making this data useful
will require far more than
high-density, high-throughput storage devices.  The traditional directory
services based on file names will yield to archive services coupled with
digital libraries that can search based on content.
\item
{\bf Special-purpose services.}
Special-purpose services such as networked instruments,
production facilities for image/video/CD-ROM output, and access to
``turnkey" computing services will allow novice users to take advantage of
popular and proven computational science codes. The community will need to
focus on providing high performance per application, as well as on
maximizing access for training and software development.
In addition, 
tele-classrooms and workshop facilities will
allow groups of users to congregate in one or several locations for
conferences, training events, symposia, etc.  These tele-centers will
provide for the distribution of meetings among major tele-centers as well
as participation by individuals from their home workstations.
\item
{\bf Access services.} Access services are emerging that will have a profound impact on
the national infrastructure.
\marginpar{Emphasis must be placed on the security of computer data.}
In particular, packet video capabilities will
be bundled in workstations over the next several years, with an estimated
$O(10^4)$ systems capable of generating data streams of roughly 1 MB/s and
$O(10^2)$ systems capable of generating up to tens of megabytes per second at high
resolution.  These will produce an aggregate national network requirement
that may become the driving force of the network infrastructure.
Additionally,
within three years, $O(10^3)$ laptop systems will be capable of
generating 1 MB/s data streams, and $O(10^4)$ systems will be generating
10 kbits/s streams; both sets will take advantage of wireless
connections.  The infrastructure must be able to accommodate these mobile
systems in terms of both capacity and straightforward access.
\end{itemize}

All these
resources and services
must be demonstrable secure 
and trustworthy enough to be heavily used by U.S. industry,
government entities, and even many educational institutions.
Strong emphasis must be placed not only on security of sites and data but
on robust authentication of communication and access.  Advanced
capabilities such as policy-based and type-of-service data routing and
access will be required.  Research is needed on how to instrument the HPCC
infrastructure to support security (e.g., auditing technology) and on how
to better understand (and minimize) the corresponding tradeoffs in speed
and ease of use.

\paragraph{Technology Infrastructure: Software and User Environment.}
Even for very advanced scientific researchers such as those
involved in Grand Challenge teams, the user interface to the
infrastructure must allow them to focus on the scientific problems and not
on the logistics of using the infrastructure.
Research is therefore needed in several key areas:
\begin{itemize}
\item
{\bf National file system.}
A fundamental first step toward integration of the infrastructure is to
provide a national file system---allowing users to access their data,
regardless of location, with consistent identifiers, or ``pathnames." 
\marginpar{A fundamental final step toward integration of the
infrastructure is to provide a national file system.}
The implementation of such an infrastructure will require considerable
coordination.
\item
{\bf Better operating systems, compilers, and code development,
optimization, and execution environments for scalable systems}.
The Grand Challenge teams are spending much effort on porting and
optimizing applications for scalable systems. This experience should be
captured through software sharing and through involving outside computer
scientists and software developers in the projects.
\item
{\bf Intelligent interfaces.}
The many separate display standards, image and file formats,
directory/indexing mechanisms, and other information storage techniques in
use today must be hidden from the user by intelligent interfaces.
At the same time, coordination among information providers is
necessary to ensure that the legion of information sources within the
national infrastructure does not become the digital Tower of Babel.
\item
{\bf Support for loading and selecting computer resources.}
Services that allow the user to select the optimum computing resource at a
particular moment, 
as well as services for allowing experiments to span several noncollocated
processing resources, will be required.
\item
{\bf Data analysis
tools with intuitive user interfaces.}    Visualization and other data
analysis systems designed over the past several years for today's datasets
will not scale to the data sets being envisioned by the Grand Challenge
teams.  The sheer quantity of data to be produced by these Grand Challenge
experiments will require an entirely new data analysis paradigm.
\item
{\bf Virtual environments.}  Immersive technologies such as
head-mounted or ``walk-in" displays  have the potential
for both data analysis and collaboration, perhaps enabling a new generation
of teleconferencing.  The impact on the infrastructure of multiple,
synchronized high-definition images will be significant compared even with
single high-definition image streams, much less with today's
relatively low-resolution image streams.
\item
{\bf ``Team'' software.}
Systems that will allow multiple team members to cooperatively
analyze large data sets will be needed in order to allow these teams to
work together effectively despite their geographic separation.
\end{itemize}
\paragraph{Human Infrastructure.}
The HPCC human infrastructure includes
supercomputer centers, science and technology centers, Grand Challenge
teams, the gigabit network testbed community, and
the Internet community.
\marginpar{Teraflops computers require a well-balanced technological and human infrastructure.}
These groups provide more than
supercomputer access, also furnishing the expertise needed to ``harness"
scalable computers, effective peer-review mechanisms for computational
science, extensive training and documentation, software,
information, and data repositories, and development activities in user
interfaces and computing environments themselves.  These organizations also
serve as intellectual centers, providing environments with long track
records of catalyzing interdisciplinary computational science teams.


\subsection{Findings}
The working group proferred several suggestions to support and enhance both
the technology infrastructure and the human infrastructure.


\paragraph{Technology Infrastructure.}
It is important to focus not
only on the visible components, such as teraflops computer systems,
but also on
a well-balanced structure of networks,
storage systems, and software environments that allow users to effectively
access and utilize these teraflops systems.
To maintain this balance, we make the following suggestions:
\begin{itemize}
\item
Interconnect
major national resources such as supercomputers, archives,
and data analysis environments with network rates
of at least 622 Mbits/s by FY95 and at least 2.4 Gbits/s by FY96.  Major
universities and regional and corporate laboratories should have network
access mechanisms that allow for peak rates of several hundred
megabits per second by
FY95 and up to one gigabit per second by FY96.  The typical university sites should have
peak capabilities of no less than tens of megabits per second by FY96.
\item
Fund
coordinated efforts among major data and information providers in
areas such as transparent interconnection of digital libraries and
standards for information indexing, storage, and display.
\item
Integrate
capabilities such as desktop video
conferencing, virtual environments, and mobile and wireless hosts, and
coordinate these capabilities with
major national testbeds.
\end{itemize}

\paragraph{Human Infrastructure.}
The human infrastructure for HPCC provides fundamental reservoirs of
expertise. These have a gestation period of at least two
generations of technology, and their continuity assures smoother
transitions from one generation of technology to another.
We suggest that the federal HPCC program provide stable and long-term
funding
for new
initiatives as well as stable base-level funding for ongoing operational
support.


%catlett@ncsa.uiuc.edu


\chapter{Conclusions}

By bringing together applications scientists
and software developers---groups that ordinarily
simply ``pass in the night''---the 
1993 Grand Challenge workshop
encouraged the sharing of ideas and problems
in high-performance computing.

In addition to providing a status
report on current Grand Challenge research and on
recent software developments,
the workshop 
provided a forum
for identifying new mechanisms for improved software sharing and
for exchanging information
about software and hardware limitations
impeding
the High-Performance Computing and Communications program.

The researchers in the Grand Challenge community
and in
the Software Development community agreed that 
the federal HPCC program is already having an 
undeniable impact.
\marginpar{The increasing capability to handle complex problems has
already attracted industry involvement.}
Indeed, one
might almost speak of a {\it high-performance revolution},
with
widespread interest in and use of MPP systems by industry and academia;
increased focus on parallelism by workstation vendors;
growing emphasis on the need for standards;
and
almost universal concern for 
developing better products (machines, languages) to make parallel computing effective.

And while the researchers agreed that it
is probably too early 
to expect HPCC program activities
to have 
had a major impact on science and industry,
{\it exciting progress is being made}.
In particular, high-performance computing is enabling researchers to
attack previously intractable problems, such as the recent
$512^3$ simulation of compressible turbulence.

This increasing capability to handle complex problems has already attracted
the attention of industry.
{\it Industry involvement} 
was noted in numerous Grand Challenge projects, including
aerospace,
environmental science,
biophysics,
computational fluid and plasma dynamics, 
computational chemistry,
reservoir modeling, and
global computational finance.

Nevertheless, several 
{\it outstanding issues} were identified.
Foremost 
was
dissatisfaction with the current state of software tools.
A frequent lament was that
computer science researchers
are not meeting the needs of the applications researchers, that
those software tools that have been
developed for high-performance systems
usually are
left in the prototyping stage.
Indeed, some called the state of software tools ``dismal.''
A clear need exists for
better parallel debugging tools,
tools for multidisciplinary applications,
performance-monitoring tools, and 
language support to
allow users to write programs at a higher level than currently
possible.
The cause for the poor software support is clear: 
a lack of incentive.
The Grand Challenge grants currently focus on the
output of the applications rather than on the software to achieve that
output.
Academia generally does not 
encourage computer scientists simply to ``help out on a project''; 
nor do research institutions fund long-term projects
to bring a software package up to commercial quality.
Commercial vendors also are not rewarded for providing robust software tools: 
the emphasis of the HPCC program has been on more powerful machines,
even when the software is not available to make the machines truly usable.
The resulting paucity
of tools is made worse by difficulties in sharing 
those tools that do exist.
More effective mechanisms are needed for exchanging
information on tool availability and accessibility.

A second major problem is dataset management.
\marginpar{Full use of massively parallel machines will require
advances in tool design, dataset management, and visualization systems.}
The Grand Challenge studies
are generating enormous datasets (up to 8 terabytes, with
predictions in the petabyte range).
Making this data useful will require
not only 
high-density, high-throughput storage devices but also 
improved archive and directory services.
Entirely new data analysis paradigms will also be needed
to add type information and structure, enabling researchers 
to access information more intelligently and more efficiently.

Even further, specialized visualization systems will be essential
for examining the vast datasets
expected from Grand Challenge research.
Currently, several efforts are under way to develop
image-rendering software
that can be executed
directly on massively parallel processing systems.
Others are developing tools for graphics display offline.
Moving the data to workstations results in high storage and
I/O bandwidth requirements; moreover,
if the data is to be displayed at full resolution,
new techniques are needed to address
the extreme demands
placed 
on the
image-rendering engines
and the display subsystem.

Related to the
growing data size of applications is
the question of data rates.
Current HPCC applications involve I/O rates of up to 6 MB/s to archive;
this rate will increase to 100 MB/s 
in the near future.
I/O is simply not scaling 
fast enough to keep up with 
CPU speed, and is threatening to become
a major bottleneck for teraflops applications.
Further research is needed to understand 
the I/O characteristics of
massively parallel computers.
Parallel I/O benchmarks are also needed that accurately reflect application
I/O 
requirements.
The Scalable I/O Initiative sponsored by the
Concurrent Supercomputing Consortium
will provide a first step toward meeting these needs, if it is funded by
the various federal agencies.

Finally, a significant number of researchers
felt that increased emphasis should be placed on standards.
Current standards efforts include
High Performance Fortran (which
met with mixed reviews from the researchers, some of whom were extremely
skeptical about its value), and
the evolving Message Passing Interface
standard; both focus on 
ensuring that programs
are supported on a large number of
different parallel computing environments.
Further efforts are needed to
promote vendor standards for archiving large amounts of data.  An example is an
industry effort to define standard interfaces for databases to make use of
tertiary storage.

\chapter{Findings}


\begin{quote}
There is nothing
quite so frustrating for the avid do-it-yourselfer than to begin a project,
suddenly need a particular tool, but have no idea where in the house to look for it.\\
\hspace{4in}-- {\it Practical Homeowner's 1987 Do-It-Yourself Annual}
\end{quote}
Many of the
researchers at the Grand Challenge workshop
expressed this feeling of frustration
in discussing their attempts to find a suitable tool 
(whether a debugger or a performance evaluation tool),  
to use a (not-well-documented) tool efficiently, and
to reuse it (i.e., have access to it through a software
library) in other applications. 
\marginpar{The workshop participants suggested several new mechanisms
for improving the effectiveness of the HPCC program.}
Indeed, a majority of the suggestions at the workshop focused on the
problem of
development, maintenance, and distribution of tools.
Here we summarize three of the principal suggestions
and---more important---propose a mechanism for implementing each
suggestion. 

\begin{itemize}
\item
{\it Suggestion:}
Collaborative development of tools
for Grand Challenge applications should be supported.
Special attention should be given to parallel debuggers, performance
evaluation tools, 
and interactive visualization software. Such efforts must be supported for all
phases of the life cycle---fundamental research, prototyping, and development.

\noindent
{\it Mechanism:}
Support a ``Grand Challenge in Software Tools,"
and establish a Science and Technology Center for HPCC Software Tools.

\item
{\it Suggestion:}
Tools
that are ready for use outside of their
development community
should be made available in software-sharing libraries.

\noindent
{\it Mechanism:}
Establish or fund existing ``software capitalization" programs to foster the
development of research ideas and prototypes into production tools and to
ensure the maintenance of 
the software in the library.

\item
{\it Suggestion:} Tools that have had success in the research
community should be commercialized.
 
\noindent
{\it Mechanism:} 
Fund an existing organization such as OSF, the national laboratories, or the
NSF supercomputer centers; or
set up a new independent organization or industrial consortium.

\item
{\it Suggestion:} Researchers need to be kept informed of the
numerous tools that
are available.  At the same time, computer scientists must be made
aware
of the needs of applications scientists for specific tools.

\noindent
{\it Mechanism:}
Develop a parallel software tools electronic journal/newsletter (complete with an
editorial board) to publicize tool availability and tool needs.
\end{itemize}

That the computer scientist is caught between Charybdis and
Scylla---encouraged
to create prototype tools as research projects, but given
little
credit for developing production-quality tools for widespread
use---was
clearly recognized by the conference participants.
Many felt that the vendors must assume more
responsibility in providing useful tools.

\begin{itemize}
\item
{\it Suggestion:} The quality and performance of the basic HPCC tools
should be improved.
In particular, vendors should 
provide good node compilers to allow programmers to make more
effective
use of machines without resorting to assembly language coding;
provide fast multiple-precision arithmetic;
pay greater attention to symbolic and combinatorial aspects of
computing, such as fixed-point arithmetic;
provide language constructs that allow the easy definition
of complex data structures;
provide high-level, standardized message-passing routines;
provide a random number generator with their machines; and
develop a compiler infrastructure for making visible the compiler's internal
representations and transformations to other software tools.

\noindent
{\it Mechanism:}   Require computer vendors (through explicitly worded RFPs) to provide
good, 
usable tools for MPP machines.
Have federal agencies support commercial development of software tools.  
\end{itemize}

Of course, providing appropriate tools (whether from vendors or
from computer scientists) requires an awareness of what is needed.
While some applications projects explicitly made
provision
for ``computer science work,''
most do not.
The workshop participants agreed that much more interaction
is needed.

\begin{itemize}
\item
{\it Suggestion:}
New incentives should be developed to encourage computer
scientists and application scientists to interact more
closely on Grand Challenge projects.

\noindent
{\it Mechanism:}  Have HPCC projects explicitly fund the exchange of students and staff
between application and tool developers.

\end{itemize}


Software technology requirements 
certainly extend beyond tools for Grand Challenge applications
research.
Several areas were identified
at the workshop as needing increased attention.
Here we first list the various suggestions;
a single mechanism is proposed to address these suggestions. 

\begin{itemize}
\item
{\it Suggestion:}
High-performance
file systems must be developed to
ensure ubiquitous access to both conventional
and high-performance  distributed file systems.

\item
{\it Suggestion:}
Database management systems must be developed that (1) support
large objects, (2) provide
fast access to subsets of large datasets, (3) interface to tertiary
storage systems, and (4) incorporate
new indexing and access methods for scientific data.

\item
{\it Suggestion:} Methods should be developed to allow mixing of different languages in a single framework.

\item
{\it Suggestion:}
``Templates"  that  capture key computational, communication,
   and I/O characteristics of important application codes should
   be written and made widely available.

\item
{\it Suggestion:}  More emphasis should be placed on integrating support for
I/O and networked services.

\item
{\it Suggestion:}
Standards efforts 
such as the Message Passing Interface forum
should be encouraged.

\item
{\it Suggestion:}
Heterogeneous computing must be fully funded 
through large projects 
that include software,
applications, and network research.

\item
{\it Suggestion:}
Engineers and scientists must be retrained in
multidisciplinary technologies, and new researchers
must be educated in this critical HPCC area
through the establishment of multidisciplinary curricula at
universities.

\noindent
{\it Mechanism:} Increase the current federal involvement,
specifically by
providing (1) stable and long-term
funding
for new
initiatives and (2) stable base-level funding for ongoing
projects.

\end{itemize}




%survey summary

\newpage
\appendix
\chapter{Grand Challenge Requirements Document}

Prior to the three-day Grand Challenge meeting in Pittsburgh,
the Program Committee prepared a detailed list of questions
that were mailed out to the participants several
weeks before the meeting.
The four-part survey included questions on applications;
Grand Challenge requirements (resources, algorithms, tools, I/O);
the current status of tools, libraries, graphics facilities,
and networks; and the most important needs in high-performance computing.


The results of the survey, which were circulated to the conference participants
prior to the meeting,
proved an invaluable resource for focusing the discussion groups.

In this appendix, we reproduce the survey and then summarized the survey
responses. 
The individual responses are available electronically via anonymous ftp
to the address
{\tt info.mcs.anl.gov}
in the directory
/pub/tech\_reports/hpcc.
Two files are included:
{\tt survey-merged.ps}  and {\tt survey.merged.enscript}.

\vspace{.2in}
\noindent
{\large \bf Part A: Application Overview Issues}

\noindent
Please cover the following issues in the summary applications
presentation sessions.
\begin{enumerate}
\item
Brief Description of Your Application Problem.
\begin{itemize}
\item
description of the ``scientific problem"
\item
algorithms used
\item
codes (size and number)
\end{itemize}
\item
Characterization of Typical Computations Planned by GC Team.
\begin{itemize}
\item
describe ``typical" computations
\item
current/planned compute resources 
\item
I/O requirements
\end{itemize}
\item
Use of Visualization and Networking.
\begin{itemize}
\item
graphics workstations
\item
framebuffers
\item
remote computing via networks
\item
distributed computing via networks
\end{itemize}
\item
Languages and Programming Tools Currently Used.
\begin{itemize}
\item
Fortran, C, C++, others
\item
PVM, p4, Express, PCN, others
\end{itemize}
\item
Most Outstanding Problem You Wish Addressed at This Workshop.
\end{enumerate}

\vspace{.2in}
\noindent
{\large \bf Detailed Grand Challenge Requirements}

\noindent
{\bf Part B: Problem, Algorithm Description and Resource Requirements}

\begin{enumerate}
\item
Please list and briefly describe the major codes you plan to employ in the
   next 3--5 years for each of your Grand Challenge applications. How long do
   you estimate that each code would take to execute (please specify some
   current platform or estimated computational rate)?

\item
Which of the following mathematical problems are solved in the course
   of running the code?  For example:
N-body force calculation, Navier-Stokes equations,
    Magnetohydrodynamics, Schroedinger equation,
    Maxwell's equations, Eigenvalue/eigenvector calculation,
    Direct simulation of physical phenomena,
    Sequence comparison, Fourier transform,
    Other (please specify)
\item
Which algorithms do you currently use to solve your problem?  For example:
\begin{itemize}
\item
    Treatment of N-body problems--
       Fast Multipole Method (Rokhlin-Greengard, Barnes-Hut, etc.),
       Explicit calculation of N-body interaction requiring O(N**2) operations,
       Explicit calculation of N-body interaction using cutoff.
\item
    Spatial discretization of PDE-- 
       Finite element, Finite difference, Spectral,
       Finite volume.
\item
    Temporal integration of PDE: 
       Explicit solution of time-dependent PDEs,
       Operator splitting methods for implicit solution of time dependent PDEs, 
       Fully implicit time dependent PDE solvers.
\item
    Monte Carlo algorithms
\item
    PIC methods
\item
    Geometric and symbolic algorithms
\item
    Discrete event simulations
\item
    Fast Poisson solvers
\item
    Eigenvector/eigenvalue calculations: 
       QR, Jacobi, Bisection methods.
\item
    Linear system solving:
       Direct methods,
       Iterative method  (Preconditioner: none, diagonal, SOR, SSOR, 
                         ILU, Block ILU, other - please specify)
\item
    Fast Fourier transform
\item
    Other (please specify)
\end{itemize}
\item
Estimate the percentage of time associated with each of the above
   algorithms.
\item
How much computational work (time on a specified platform) is
   associated with each algorithm?
\item
How much memory is needed for typical computations?
\item
What type of data structures do you employ?
\item
If your problem uses a grid or mesh, which of the following do you use?
   	Single structured grid,
   	Block structured grid (problem solved on multiple, interacting meshes),
   	Unstructured grid.
\item
If your problem is formulated using matrices, which of the following
   do you use?
   	Dense matrix representation;
   	Banded, envelope, or skyline representation;
   	Sparse matrix representation.
\item
What are (or will be) your I/O requirements with regard to the following?
        Data (in bytes) moved to and from secondary or tertiary storage
            during program execution;
        Minimum I/O bandwidths; 
        Desired I/O bandwidths.
\item
Must you execute several different programs to solve your problems?
    If so, do these execute
	    as a single application?
	    as a pipeline?
	    on the same computer?
	    on different computers?
\end{enumerate}

\vspace{.2in}
\noindent
{\large \bf Part C:  Needs Assessments and Opinions}

{\bf I/O and Data Systems}

\begin{enumerate}
\item
What features would you most like to see in new I/O and data systems?
\item
How large is the archive (something other than flat files) for data  
    management?

\noindent
{\bf Parallel Programming Paradigms and Program Development Tools}

\item
What parallel tools or languages have members of the group used?
\item
Has the use has been minimal, moderate, or extensive?

Example software systems for next four questions:
Parallel Languages/Compilers (CM Fortran, C*, *Lisp, PCN, Strand, Forge90,
C with message-passing calls, Fortran with message-passing calls, etc.);
Parallel Programming Libraries or Tools (Express, PVM, p4, Linda, Parti);
Performance-Monitoring Tools (e.g., Gauge, Pablo, ParaGraph, Upshot);
Parallel Debuggers (specify machine if the tool is supplied by the vendor);
Other software tools that pertain to parallel machines (name the tools)
\item
Which tools do you find are most useful?
\item
Which tools are conceptually poor but are well implemented
(i.e., the tool does not do anything you need to have done,
but does its tasks reliably)?
\item
Which tools are conceptually good but poorly implemented? 
\item
What is the source of your parallel programming support software?

\noindent
{\bf Performance Evaluation of MPP Applications}
\item
How do you characterize the performance of your application (MFlops,
     size of problem attempted, CPU time per model timestep, other)?
\item
How do you know that your code is running at a reasonable
     performance level?
\item
Which is more important: faster turnaround on existing problem sizes,
     or larger problem sizes with same turnaround time as today?
\item
Do you expect to be able to use the same numerical methods on serial
     machines and MPPs for your application areas?

\noindent
{\bf Discussion of Libraries} 
\item
Do you use numerical libraries or solvers
      (e.g., LINPACK, LAPACK, EISPACK, PCGPACK, SPARSEKIT)? If you
       do use such codes, please specify which.
\item
Do you use commercial or publicly distributed applications codes
      (e.g., GASP, NASTRAN)?

\noindent
{\bf Graphics and Visualization}

\item
How do you (or will you) view your results?
       Small set of fixed or floating point numbers,
       Set of 2-D pictures,
       Set of 3-D pictures,
       Animations
\item
What fraction of your group's time is spent analyzing the results of runs
       vs running them vs developing code?
\item
What is the biggest problem in your postprocessing environment?
\item
What capabilities do you want in a visualization system?
\item
Is it important to be able to generate graphics directly from an
      MPP system?

\noindent
{\bf National High-Performance Computing and Communications Infrastructure}
\item
How often do you use networks of workstations on a single application?
     If you do use networks of workstations on a single application,
     please answer the following:
\item
What performance do you achieve?
\item
Do you wish to use heterogeneous networks of machines,
     or are you using them because you do not have access to
     true MPP systems of sufficient capacity?
\item
What software tools do you use for distributed computing?
\item
Would you prefer to compute on a network of workstations, on a tightly
     coupled centralized MPP, or on a network of MPPs?

\noindent
{\bf Multidisciplinary Applications}
\item
If your problem involves execution of several different programs,
    which do you expect to parallelize?  How will you exchange data
    between programs (e.g., files, sockets, message-passing)?

\end{enumerate}

\vspace{.2in}
\noindent
{\large \bf Part D:  Most Important Needs}

Indicate the areas where progress on your research can be accelerated
if you (and your colleagues) could be assisted by other professional
communities, including those of mathematics, computer science, and
other computational science disciplines, and also by the computer
industry.  In particular, are there factors that are currently
limiting your productivity (you may use the items on the following
list as examples, adding to them where appropriate)?  In your
response, be as specific as possible concerning the character of the
problem(s).

\begin{itemize}
\item
Mathematical description of the problem(s)
	- theoretical or practical limitations
\item
Numerical limitations
	- word size; fraction or exponent limitation
\item
Algorithmic limitations 
	-  require new approaches, more generality, higher performance, etc.
\item
Library inadequacies
\item
Programming model inadequacies
\item
Language/compiler limitations
	- inadequate languages: what do you need?
	- inadequate compilers: in what way?
\item
Tool limitations
	- programming, debugging, performance monitoring, graphics,
	  data management
\item
Turnkey packages that you would like to see developed
\item
Programming environment limitations
	- too cumbersome, not responsive, inadequate procedures, etc.
\item
Hardware limitations
	- performance, memory, I/O, connectivity
\end{itemize}

If you had one ``software magic bullet," what would you use
it for? Please specify one of the items listed above, if appropriate.
             

\chapter{Summary of Survey Responses}

{\it Al Brenner (Supercomputing Research Center); Alok Choudhary (Syracuse); Tom Morgan, Gail Pieper, and Rick Stevens (Argonne National Laboratory); Joel Saltz (Maryland)}


\vspace{.15in}
\noindent
{\large \bf 1. Overview}

We received 28 completed surveys.
This report provides a summary of some of the issues
that have a particularly strong potential bearing on directions that
need to be taken in systems software and hardware.

\vspace{.15in}
\noindent
{\large \bf 2.  Issues }

The teams identified a wide range of issues that
they believed important:
\begin{itemize}
\item
development of capabilities to allow asynchronous data transfer while processors are
engaged in computations;
\item
faster parallel I/O;
\item
development of robust compilers for parallel architectures that abstract away low-level details of machines and algorithms, without catastrophic performance losses;
\item
extension of HPF to cover nonstandard data structures (block-structured data, unstructured data, etc.);
\item
compilers that take better advantage of RISC or vector processor architectures;
\item
decreasing communication latency;
\item
support for collective communications;
\item
tools and techniques for heterogeneous environments; and
\item
methods for load balancing.
\end{itemize}

\vspace{.15in}
\noindent
{\large \bf 3.  Languages, Programming Tools, and Libraries Currently Used}

Although there are almost as many different languages and tools being used by GC teams as the number of teams, the following briefly describes the most commonly used
languages and tools.
Fortran and C are both widely used:
many teams use both (20 teams use Fortran, and 20 use C); 13 use C++,
although no team reported using C++ as their only programming language.
Message-passing libraries are widely used (22 teams reported using some kind of message-passing library, including libraries supplied by vendors, PVM, and PICL).
Ten teams reported using a vendor-supplied parallel Fortran (typically CM Fortran);
5 teams reported using a vendor-supplied parallel C (typically C*); 5 teams
reported using extensions for programming shared-memory machines.

Other languages and tools used by some teams include PCN, CC++, and pFortran.

Other tools used include {\tt p4}, Linda, DTM AVS, PARTI, and Smalltalk
(one or two users each).

CMSSL is extensively used by a number of teams.
A few teams use BLAS, LINPACK, LAPACK, and EISPACK.
A number of groups have developed their own C++ class libraries.

\vspace{.15in}
\noindent
{\large \bf 4.  Data Structures Employed}

The directions in Grand Challenge-related systems software research must be motivated by carefully considering the nature
of the algorithms being tackled by the teams.
Out of the 28 Grand Challenge teams responding to the survey, 26 reported that part of
their work involves solving well-structured problems, including structured mesh
and dense matrix calculations.
However, 16 of the teams also report that some of their calculations involve problems with irregular indirection patterns
including unstructured mesh and general sparse matrix calculations,
traversal of linked lists, and calculations involving explicitly enumerated atom lists (in
molecular dynamics problems).
Approximately 12 teams reported that some of their calculations involved block-structured grids.
in some cases the intergrid data transfer patterns are static; in at least 4 cases, the
patterns change during the course of the problem.
There were 4 teams that reported using tree-structured algorithms such as
fast multipole algorithms.

\vspace{.15in}
\noindent
{\large \bf 5.  Software Support Requirements}

In general, most teams felt that most of the available tools are primitive and inadequate.
There is very substantial interest in portable compilers able to give ``reasonable" performance.
There is a need to provide language support to allow users to write programs at a higher level than what is currently supported on parallel
architectures.
Many users want to be able to specify parallelism and data and task
decompositions at a high level.
Debuggers and performance tools were requested by almost all the teams.
A large majority felt that software and architectural support
for high-performance I/O is inadequate.

\vspace{.15in}
\noindent
{\large \bf 6.  Compiler, Programming Tool, and Library Requirements}

Portability of compilers across various machines was the most frequently requested feature.
Fine tuning codes for different architectures was a major problem faced by most of the teams.
Many researchers complained of buggy eta compilers.
Quite a few of the responses criticized the quality of code produced by the compilers.
Several responses asked for better node compilers.

Most of the responses pointed out the need for a much larger set of tools.
Debugging tools and performance-monitoring tools were frequently given the highest priority.
Quite a few responses mentioned the need to standardize tools across different
architectures.
Graphics tools were also considered highly inadequate.

I/O libraries were requested by many teams.
Image processing and display libraries were also requested by a few teams.

\vspace{.15in}
\noindent
{\large \bf 7. Networking and Visualization Requirements}

Msot teams access MPP through what are perceived to be inadequate networks.
many plan to move to faster local networks.
Graphics and visualization for examining output create particularly troublesome
network problems.
Most researchers report that they carry out visualization off-line;
this results in high storage and I/O bandwidth requirements.
Dataset sizes can range from 1 MB to 8 TB.

\vspace{.15in}
\noindent
{\large \bf 8. Algorithmic Requirements}

A number of researchers felt that new algorithms need to be developed, for example:
\begin{itemize}
\item
efficient algorithms to find eigenvalues in 
parallel,
\item
fast Poisson solvers for massively parallel machines'
\item
methods for efficiently solving sparse linear systems,
\item
efficient domain decomposition strategies,
\item
algorithms to minimize communication time for traversal of
distributed data structures, and
\item
static and dynamic load-balancing methods.
\end{itemize}

%requirements questionnaire common complaint was that parallel algorithms are just that: parallel versions of existing sequential algorithms.
What appear needed are truly parallel algorithms that are not directly connected with the earlier sequential formulations.

\vspace{.15in}
\noindent
{\large \bf 9. Hardware Requirements}

Lack of sufficient I/O bandwidth on parallel machines was cited as the biggest single hardware limitation.
Many users believe that the I/O bandwidth at each processor must at least equal that of sequential machines.
Some users do not currently face I/O performance bottlenecks but predict that
the I/O capabilities will at some point become a bottleneck.
A number of users felt that parallel machines should offer a single I/O interface.
Users felt that machines currently have a wide variety of I/O interfaces and that this lack of standardization causes problems in
porting programs onto different machines.

Many users feel that the machines they are using should have a higher communications bandwidth
and lower communication latency.
Increased message latency was cited as a particular bottleneck.
Another frequently cited limitation was a scarcity of usable memory at each processing node.

Some users felt the need for better floating-point performance and larger cache size for the
processors.
Other users were unhappy that individual processors could achieve only a fraction of the possible
peak performance on most real problems.

\vspace{.15in}
\noindent
{\large \bf 10. Mathematical Problems Solved}

The Grand Challenge teams are solving a very wide variety of problems drawn from various scientific disciplines.
In this brief summary of the survey results, we do not attempt to give
a comprehensive picture of what physical or biological problems each team is attempting to solve and
the  mathematical formulations and numerical algorithms they employ.
We do present a partial list of the mathematical problems
being attacked by the Grand Challenge teams:

\begin{tabular}{l}
    N-body force calculation\\
    Solutions of the Schroedinger equation\\
    Solutions of the Dirac equations\\
    Flow solver: Navier-Stokes equations\\
    Hydrodynamics\\
    Magnetohydrodynamics\\
    Maxwell's equations\\
    Stochastic PDE (Brownian dynamics)\\
    Poisson solution of implicit radiation transfer equations\\
    Radiative transfer through absorbing and scattering media\\
    Conductive solver: conduction equation\\
    Integration of well-behaved, stiff, and beyond stiff ODEs\\
    Controls: 4th-order system of equations\\
    Shapiro filter\\
    Numerical integration of 2nd-order differential equations\\
    Integro-differential equations (aerosol physics)\\
    Eigenvalue/eigenvector problems\\
    Solution of linear systems\\
    Nonlinear parameter optimization\\
    Fourier transforms\\
    Moving body dynamics: 6 degrees of freedom \\
    Convolutions\\
    Radiative transfer equations\\
    Sequence comparison\\
    Direct simulation of physical phenomena\\
    Multidimensional pattern recognition\\
    3D ray tracing through diffuse media\\
    Coupled structural dynamics and Navier-Stokes equations\\
    Coupled control, structural dynamics, and system identification \\
    Coupled Maxwell and Ginsburg-Landau equations\\
\end{tabular}

\newpage
\chapter{Abstracts of Talks}

\begin{flushleft}
{\large \bf Panel A -- Environmental and Earth Sciences} 
\end{flushleft}
\hspace{.2in}Chair: {\it Joan Novak, EPA}


\vspace{.2in}
\centerline{\bf A DISTRIBUTED COMPUTATIONAL SYSTEM FOR}
\vskip .08886 truein
\centerline{\bf LARGE-SCALE ENVIRONMENTAL MODELING} 
\vskip 16pt
\centerline{Armistead G. Russell}
\centerline{Carnegie Mellon University}
\smallskip
\vskip 16pt

%
%
Environmental issues are of great societal concern.
In the United States alone, more than \$30 billion per year is spent
on air pollution control.
A key premise of this proposal is that high-performance computing
environments and numerical experimentation can
play vital roles not only in unraveling the complex physics and
chemistry occurring in the
environment but also in shaping the design of cost-effective public
policies.
Most advanced environmental models are very computationally and data
intensive.
Distributed computer systems offer the potential to revolutionize the
current approach to environmental modeling.

The proposed enabling technologies---giga-performance heterogeneous
computing systems,
advanced software environments, parallel architectures, and Gbits/s
networks---offer
the possibility not only of increasing the resolution but also of
providing the power needed to perform systematic
sensitivity/uncertainty
analyses, assimilate observational data, and incorporate more
realistic treatment of the underlying physical and chemical processes.

An initial application of a distributed computing environment is proposed that
exploits high-speed communications networks and special-purpose
architectures.
Research will be carried out to improve the models and their mapping onto
advanced architectures and to create a flexible software environment
that will enable more widespread use of the tools.  Extensive
collaboration with industry, government, and other universities is
planned.

%From: Daewon Byun <bdx@flyer.ncsc.org>
	

%
%
%
\vspace{.2in}
\centerline{\bf COMPREHENSIVE EULERIAN AIR QUALITY MODELING} 
\vskip .08886 truein
\centerline{\bf OF MULTIPOLLUTANT SPECIES IN THE ATMOSPHERE}
\vskip 16pt
\centerline{Kenneth Galluppi and Daewon W. Byun}
\centerline{North Carolina Supercomputer Center and}
\centerline{Environmental Protection Agency}
%ACMD/AREAL
%MD-80
\smallskip
\vskip 16pt
%Research Triangle Park, NC 27711

	
Current EPA comprehensive air quality models simulate 
effects of atmospheric dynamics (transport, diffusion, and 
deposition), cloud processes (cloud mixing, aqueous chemistry, 
and wet scavenging), photochemical and thermal reactions 
(gas phase reactions of NO, NO$_2$, CH$_4$, CO$_2$, formaldehydes, 
Volatile Organic Compounds, nitrates, SO$_2$, sulfates, and 
oxidants), reactions of atmospheric particulates, and 
anthropogenic and biogenic emissions on air quality.  
Many of the inputs to the air quality models are provided 
by meteorological numerical models or objective analyses 
of observations.  Because of strong dependency of air 
quality models on meteorological models, both modeling 
activities are coordinated.

One of the objectives of the Models-3 project is to develop
next-generation air quality models with upcoming HPCC 
technology. The models will be used to enhance progress 
in related science and algorithms as well as to provide 
easy-to-use tools for environmental policy decision-making.   
EPA's Grand Challenge team is assigned to design 
a modeling system that is generalized to address multiple 
air quality issues from urban to regional scales and 
that is modular to enable continuous upgrading of 
science components. At a later stage, the models will be 
expanded to handle multimedia pollutant issues.
	

\vspace{.2in}
\centerline{\bf LINKED WATER AND AIR QUALITY MANAGEMENT}
\vskip 16pt
\centerline{Thomas O. Barnwell, Jr., and Robin L. Dennis}
\centerline{U.S. EPA}
\smallskip
\vskip 16pt



The objectives of this project are to
assist th
the EPA's Chesapeake Bay Liaison Office in developing
nitrogen control strategies for mitigating
eutrophication in the Chesapeake Bay and to study the development of
multimedia pollutant control strategies for air and water emissions.
The project will link the MM4 Mesoscale Meteorological Model, the
RADM Regional Acid Deposition Model,
the HSPF Hydrological Simulation
Model -- FORTRAN, a 3-Dimensional Hydrodynamic Model of the Bay,
and a 3-Dimensional Water Quality Model of the Bay.

Initial emphasis will be on linkage of RADM and HSPF.
Improvements in RADM will focus on development of a finer grid scale to
better resolve deposition processes and emission sources, making RADM
very compute intensive and prompting its movement to a massively parallel archit
ecture.
Additional work in HSPF
will improve its ability to track nitrogen deposition
in forests
hrough the development of
process descriptions for nitrogen transport and transformation in
forested ecosystems.
These improvements are necessary to provide the proper linkage
between atmospheric deposition estimates and nitrogen inputs to the Bay
throughout the terrestrial pathway.
These improved codes will
be tested in the Chesapeake Bay Watershed.

We will be exploring the use of the linked models in distributed and heterogeneous
 computing environments.
Issues investigated will include transferring information between models both th
rough simple data linkages and through more dynamic coupling.
Also being developed are more intuitive model interfaces and
decision support systems for generating control scenarios and
analyzing model output, as well as
exploring visualization techniques for model calibration, validation, and
assessment of control strategies.

%skua.mcs.anl.gov by antares.mcs.anl.gov with SMTP id AA23577

%
%
%
\vspace{.2in}
\centerline{\bf FUTURE OF HIGH-RESOLUTION WEATHER PREDICTION}
\vskip .08886 truein
\centerline{Alexander MacDonald}
\centerline{NOAA/Forecast Systems Laboratory}
\smallskip
\vskip 16pt
A review of the past 30 years of weather prediction shows
that progress in large-scale fields such as the 36-hour 
500-millibar height has been quite remarkable.  In the same period,
improvements of precipitation forecasts, such as the 0- to 24-hour
heavy-precipitation prediction issued by the National
Meteorological Center, has been surprisingly modest.  It is well
known in the meteorological community that accurate prediction of
cloud and precipitation is a difficult problem, both in the short
term (will it rain tomorrow?) and the long term (will the central
United States become much drier in the year 2050 as a result of greenhouse
effects?) predictions.

NOAA's Forecast Systems Laboratory
is taking a three-step approach to improving 
weather prediction.  These steps are to greatly increase
observations by using (1) heterogeneous data, (2) four-dimensional
assimilation, and (3) massively parallel computers for
high-resolution mesoscale models.  Each step will be reviewed,
with current results from small and regional scales presented,
and implications for global models given.

High-performance computing and communications will improve
weather prediction if a balanced program of increasing
observations, meteorological models knowledge, and computing
speed is pursued nationally. 

\vspace{.2in}
%\nopagenumbers
\centerline{\bf  
 RESERVOIR MODELING FOR POROUS MEDIA:}
\vskip .08886 truein
\centerline{\bf PARTNERSHIP IN COMPUTATIONAL SCIENCE}
\vskip 16pt
\centerline{ Mary Wheeler}
\centerline { Rice University}
\smallskip
\vskip 16pt

Many of our groundwater resources are threatened by sources of
pollution.  The challenge is to develop efficient and accurate
computer models for predicting the distribution and movement of
subsurface fluids in underground geological structures, and to predict
contaminant transport through underground heterogeneous porous media.
Important aspects of the problem include the interaction between
subsurface and surface water, the spatial locations of contaminant
sources, chemical reactions that occur in the subsurface (e.g.,
sorption, precipitation, dissolution, complexation, biological
transformation), and the lack of measured data to adequately
characterize the aquifer.  Successful models should aid in the
development and monitoring of remediation processes.

The first main problem is the numerical computation of fluid flow and
transport in porous media, which involves solution of a coupled system
of nearly hyperbolic and nearly elliptic parabolic equations for the
fluid pressures and concentrations of dissolved substances.  Emphasis
is on efficient, high-accuracy solutions, especially in flows
characterized by discontinuous or nearly discontinuous fluid and/or
porous material behavior.
The second main problem is to quantify the uncertainty associated with
simulation results as a function of the uncertainty of medium and
fluid parameters, especially when important measurement data are
unavailable.

We plan a two-phased approach.
A typical first-phase computation will involve a computation of
single phase (i.e., in the saturated zone below the water table) fluid
motion involving possibly several fluid components which react and
diffuse.  DOE sites suffering from groundwater contamination will be
targeted for computation.  Second-phase computations will take into
account multiphase flow both in the vadose zone above the water table,
and in the saturated zone.  These problems typically require the
solution of hundreds of thousands to millions of nonlinearly coupled
unknowns, for simulation times from days to hundreds or even thousands
of years.

This is a collaborative effort with
Oak Ridge National Laboratory, Brookhaven National Laboratory,
Rice University, SUNY Stony Brook, Texas A\&M University,
and the University of South Carolina.



\vspace{.2in}
\centerline{\bf RESERVOIR MODELING FOR POROUS MEDIA:}
\vskip .08886 truein
\centerline{\bf LATTICE GAS COMPUTATIONS }
\vskip 16pt
\centerline{ Kenneth Eggert and Shiyi Chen}
\centerline { Los Alamos National Laboratory}
\smallskip
\vskip 16pt

We are developing enhanced oil recovery strategies.
Our focus is on
lattice gas computations.
We use two major codes to model pore-scale processes.
The first code,
the lattice Boltzmann multiphase porous media model,
has the capability of modeling multiple phases of fluids flowing through complex porous media.
It includes variable contact angle and variable surface interface tension
capabilities.
The second code,
the percolation-network model, bases the description of both the medium and the displacement process at 
a somewhat larger scale.
Both codes are run on the CM-200.


\newpage
\centerline{\bf TEN- AND HUNDRED-YEAR CLIMATE PREDICTION}
\vskip 16pt
\centerline{Dave Bader}
\centerline{Pacific Northwest Laboratory}
\smallskip
\vskip 16pt


The goal of our project is to predict climate change on
ten- to one-hundred-year time scales.
To this end, we
use models capable of estimating the range of possible future
climate variables on spatial scales that are relevant to human
activities to guide planning and policy development.  The current approach
is to couple models describing the atmospheric circulation, the ocean
circulation, land surface material and energy exchange, and sea ice
dynamics and thermodynamics.  Algorithms and codes for each of these climate
subsystems are different.  The largest and most complex codes are the general
circulation models (GCMs) of the atmosphere and ocean.  The atmospheric
codes typically include submodels for the land surface, and the ocean models
can include sea-ice submodels.  A variety of algorithms and approaches is used
for GCMs, which must be capable of simulating geophysical fluid dynamics
on appropriate scales.  The approaches include finite-difference and spectral
formulations of the geophysical fluid systems in spherical coordinates.  The
model codes typically run 10,000 to over 50,000 lines of Fortran code.


We are planning three types of computation:
finite-difference atmospheric GCM,
spectral-formulation atmospheric GCM, and
finite-difference ocean GCM.
The enormity of these computations is illustrated by our effort in
developing a finite difference atmospheric GCM.
Currently, our model
requires about 8 single-CPU-hours per simulated year on a CRAY C90.
The simulation we are planning---which will involve a ten-year
simulation of the global atmosphere and land-surface, with a
three-dimensional grid of about $10^5$ cells, and prescribed ocean and
sea-ice distributions---will
require 140 single-CPU-hours. 

\vspace{.2in}
\centerline{\bf DEVELOPMENT OF AN EARTH SYSTEM MODEL:}
\vskip .08886 truein
\centerline{\bf ATMOSPHERE/OCEAN DYNAMICS AND TRACERS CHEMISTRY}
\vskip 16pt
\centerline{C. Roberto Mechoso}
%\centerline {Department of Atmospheric Sciences}
\centerline{{University of California - Los Angeles}}
%\centerline{Los Angeles, CA}
\smallskip
\vskip 16pt

The goal of this project is to develop a coupled
earth system model (ESM) of
atmospheric and oceanic circulations and chemical tracers.
%Earth System Model (ESM), formulated for parallel computer
%environments, will be applied to study problems in climate,
%climate change, and climate/chemistry interactions, including the
%general circulation of the coupled atmosphere/ocean system, global
%distributions of greenhouse gases, and global ozone perturbations.
The model development uses the following major
components:  (1)  the UCLA general circulation model of the
atmosphere (AGCM),  (2)  the GFDL/Princeton University general
circulation model of the ocean (OGCM),  (3)  the NASA Ames/UCLA
chemical/aerosol tracer model (CATM),  and (4)  the Lawrence
Livermore National Laboratory atmospheric chemistry model
(LACM).  Versions of these component models are 
operational, and preliminary coupling of the components has been
carried out.

The ESM will have a highly modular structure, in which
components can be interchanged or added with a minimum of
computational difficulty.  The code will be highly optimized,
including local parallelization within each module as well as
between modular components.  Finally, the coupled model will be
configured to work in a distributed (heterogeneous)
computer environment across high-speed networks.  The AGCM, OGCM,
CATM, and LACM are all grid-point models, which is an advantage in
parallel architectures. An important issue to be addressed in model
development is load balancing among processors, which may
become more critical as the parameterizations of physical and
chemical processes grow in sophistication and the computational
load between model regions becomes more variable.

The ESM will be used to study the climate system, climate
change, and climate/chemistry interactions.  This work is divided
into studies of
the global chemistry of the atmosphere and
the fundamental coupled dynamics of the global
atmosphere and ocean system.
The interannual variability of the coupled atmosphere
and oceans, including the seasonal cycle and transients up to
decadal time scales, will be simulated.  We plan to couple
the AGCM and CATM to study global ozone depletion, including the
effects of atmospheric aerosols on the chemistry of the upper
atmosphere. 
The LACM will be used to develop and test photochemistry, and
heterogeneous chemistry algorithms will be used in the coupled
AGCM/CATM.  The LACM will also be employed for tropospheric
chemistry simulations using dynamical fields from the coupled
AGCM/OGCM.

%This project will involve important algorithm development
%for future large-scale Earth System Science simulation, and will
%provide unique scientific insights into coupled
%dynamical/chemical/microphysical processes that contribute to
%global climate change and ozone depletion.



%From lys@zion.jpl.nasa.gov Tue Apr 20 09:09:39 1993


\vspace{.2in}
\centerline{\bf DEVELOPMENT OF ALGORITHMS FOR CLIMATE}
\vskip .08886 truein
\centerline{\bf MODELS SCALABLE TO TERAFLOPS PERFORMANCE}
\vskip 16pt
\centerline{Dan Kowalski}
\centerline{NASA/Goddard Space Flight Center}
\smallskip
\vskip 16pt


Our project involves long-term climate modeling using a
coupled ocean, atmosphere, land-surface, and sea-ice  model. The atmosphere
and ocean models use the Arakawa C and B grid finite-difference
schemes, respectively, for calculating fluid dynamic evolution. The
atmospheric model includes multiband short- and long-wave radiation
parameterizations; a stability-dependent, turbulent boundary layer
scheme; the Relaxed Arakawa Schubert (RAS) scheme for penetrative
convection; and a mosaic land surface model. The ocean model is a
reduced-gravity, layered ocean model and includes a surface mixing
parameterization at the ocean's surface.

Currently, a typical coupled ocean/atmosphere run of 30 simulated
years requires about 225 single-node CRAY Y-MP hours. We plan 
an initial implementation of several prototype problems from our coupled
climate model on several testbed
massively parallel processors, including
a six-node CRAY Y-MP, MasPar MP-1, Intel DELTA, and CRAY C-90.
We will then conduct
a full implementation of an improved coupled model. Some of the prototypes
are atmospheric dynamical core, ocean dynamical core, shortwave
radiation parameterization, cumulus parameterization, mosaic land
surface model, and model diagnostic I/O.


%
%
%
\newpage
%\vspace{.2in}
\centerline{\bf HIGH-PERFORMANCE COMPUTING AND 4D DATA ASSIMILATION:}
\vskip .08886 truein
\centerline{\bf  IMPACT ON FUTURE AND CURRENT PROBLEMS}
\vskip 16pt
\centerline{Richard B. Rood; Presenter Peter Lyster}
%Data Assimilation Office
\centerline{NASA/Goddard Space Flight Center}
%Greenbelt, MD 20771
%(301)-286 8203

%Presenter:              Peter M. Lyster
%e-mail; lys@zion.jpl.nasa.gov
%(301)-805 9847

%Note: If this abstract needs to be shortened, then you can lop-off
%the last paragraph, or the last two paragraphs; but please no more!

\smallskip
\vskip 16pt

Our proposal focuses on the Grand Challenge of four-dimensional
data assimilation to produce research-quality datasets for earth
science studies.  This involves the collection of diverse earth-observational data sets and the incorporation of these data into
models of the ocean, land surface, and atmosphere, including
chemical processes.  Ultimately the goal of data assimilation is
the calculation of consistent, uniform spatial and temporal
representations of the earth's environment that can be used for
scientific analysis and synthesis.

The proposal has two major thrusts.
First, we propose to apply leading research in parallel software and
algorithms to a number of existing techniques for assimilation
that can benefit from greatly increased computer power.  The
operational global atmospheric data assimilation system of the
Data Assimilation Office at the Goddard Space Flight Center,
along with techniques for tracer advection and chemical analysis,
will be implemented on parallel software/hardware systems and
assessed both for performance and for development effort
required.  The product of our work will be a new generation of
integrated, operational models running on heterogeneous parallel
systems at orders of magnitude greater performance than the
current operational system.  The assimilation of reactive
constituents is a high priority in NASA  environmental
observation programs, including the Upper Atmosphere Research
Satellite and the Earth Observing System, and this
will be an important step in making the assimilation of reactive
constituents computationally viable.

Second, we propose to explore new data assimilation methodologies that
can be implemented only with computers that far exceed the
capacity of today's machines.  We will emphasize the development
of the first three-dimensional full Kalman filter scheme. We
expect to exploit existing parallel system software as well as to
develop techniques that arise out of the special requirements of
data assimilation. Three-dimensional Kalman filtering schemes
will challenge the limits of teraflops machines for the
foreseeable future.

The data assimilation effort at NASA requires the extension of
atmospheric data assimilation techniques to many disciplines of
Earth Science.  In order to develop a successful assimilation
system for Earth Science, there will be a continual need to
process and reprocess data sets with ever-improving and more
complete assimilation systems.  There will also be a requirement
to diagnose the quality of the data sets.  Data assimilation
provides the most computer-intensive undertaking in NASA Earth
Science research.  It is essential that assimilation algorithms
and models be able to exploit the future computing environment.

%The problems described in this proposal provide a basis for
%future assimilation work.  The first  thrust of the proposal
%provides a mechanism to study the implementation of both the
%fluid- dynamical and physical parameterization components of our
%Earth system models.  The second thrust investigates the
%assimilation technique that theory says is the most powerful.
%Together they define a core from which assimilation techniques
%can be extended to further applications.  The development of
%these systems in a parallel computing environment now assures the
%computational vitality of future assimilation systems.


%

%From Gary_Pope@humble.pe.utexas.edu Tue Apr 20 15:14:45 1993

%
%
%\newpage
%\centerline{\bf RESERVOIR MODELING FOR POROUS MEDIA:}
%\vskip .08886 truein
%\centerline{\bf PETROLEUM AND GROUNDWATER}
%\vskip 16pt
%\centerline{Gary A. Pope}
%\centerline{University of Texas}
%\smallskip
%\vskip 16pt
%

%We are developing parallel algorithms for modeling flow in permeable
%%media.  Such models include restoration of ground water contaminated by organic
%chemicals and reservoir simulation of enhanced oil recovery.  These models are
%essential for the design and interpretation of contaminant cleanup and enhanced
%oil recovery procedures.  Parallel algorithms for permeable-media models are
%required to take advantage of the large number of processors now available on
%massively parallel computers.  We are running models on parallel computers of
%distributed-memory, multiple-instruction, multiple-data type such as those
%produced by Intel (DELTA and Paragon), Ncube, and Thinking Machines (CM-5). 

%Initially, codes for running such models are based on two widely used reservoir
%simulators developed at the University of Texas.  The basic approach taken to
%parallelize these codes is domain decomposition.  Parallelization involves
%high-resolution discretization schemes.  Such schemes are important for
%efficient use of computer time and storage in obtaining adequate model
%resolution.  Parallelization of linear solver and input-output features is also
%being addressed.  ``Scale-up" with the number of processors and
%benchmarks is being estimated.  We are also developing a code for parallel machines
%that uses the technique of simulated annealing.
%The code will be applied to the 
%optimization of the characterization of permeable media (both oil reservoirs
%and aquifers).

%All of these activities involve students at the University of
%Texas and Rice in four different academic departments and will contribute
%directly and substantially to the development of HPCC education and human
%resources.


%From NOVAK.JOAN@epamail.epa.gov Mon Apr 19 12:49:39 1993
\newpage
          

%\vspace{.2in}
\begin{flushleft}
{\large \bf Panel B -- Computational Physics} 
\end{flushleft}
\hspace{.2in}Chair: {\it Tom Kitchens, DOE }

\vspace{.2in}
\centerline{\bf UNDERSTANDING SOLAR ACTIVITY AND HELIOSPHERIC DYNAMICS}
\vskip 16pt
\centerline{John Gardner}
\centerline {Naval Research Laboratory}
\smallskip
\vskip 16pt

%As a joint LCP\&FD and SSD effort,
This project
seeks to develop and implement 
advanced numerical algorithms on parallel computers in order to solve Grand 
Challenge problems in solar and heliospheric physics. 
The effort will 
augment and be leveraged by existing programs
at the Naval Research Laboratory
in both solar theory 
and computation.  We have proposed that NASA's HPCC initiative should have 
as one of its goals to understand the solar driving engine: the mechanisms 
of solar activity and the dynamics of the heliosphere.

Solar activity is the underlying driver for many important phenomena in
space physics.  UV and X-ray radiation from the sun play dominant roles in
the formation of the earth's ionosphere and are thought to be partially 
responsible for the observed variations in the south polar ozone hole.  
High-energy radiation from solar flares strongly perturbs the structure of 
the earth's upper atmosphere and thus greatly affects the lifetimes of NASA 
satellites in low earth orbit.  Planetary magnetospheres form and evolve
as a result of the interaction between each planetary magnetic field and the solar wind 
originating in the corona. Solar coronal mass ejections drive geomagnetic 
storms and are responsible for electric power disruptions on earth.  The 
ejecta are also the origin of interplanetary shocks and energetic particles.

During the next decade, solar activity will be the focus of a number of 
NASA space physics missions. The Gamma Ray Observatory is 
making observations of $\gamma$-ray emission from solar flares.
YOHKOH is returning exciting data on X-ray emission from flares and active
regions for the first time giving us detailed information on the three-dimensional structure and magnetic topology of solar flares and 
associated active regions, Ulysses is beginning its study of the solar 
wind at high latitudes, promising to provide new insights into the wind's 
acceleration mechanism and the three-dimensional structure of the heliosphere.  In the next 
few years, the Solar and Heliospheric Observatory will begin
study of the sun's inner and outer corona and the heliosphere.

To realize fully the scientific return from these missions, 
we urgently need to expand our dedicated effort in numerical and theoretical 
modeling into the massively parallel generation of computation hardware.
We propose a program of research to develop new methods for numerical 
simulations on teraflops-range parallel computers and to apply this
powerful new technology to outstanding problems in solar and heliospheric
physics.  

%From LAKE@raven.phys.washington.edu Fri Apr 16 16:32:08 1993
%
%
%\nopagenumbers
\newpage
%\vspace{.2in}
\centerline{\bf LARGE-SCALE STRUCTURE AND GALAXY FORMATION}
\vskip 16pt
\centerline{George Lake; Presenter Calvin Lin}
\centerline{University of Washington}
\smallskip
\vskip 16pt

When we observe the universe, we use the dynamics and clustering of the
luminous galaxies to infer the properties of the dark matter. Yet, even in our
largest simulations of large-scale structure ($N= 10^{6.3}$), a galaxy is
represented by $\sim 100$ particles.  As a result, it dissolves by two-body
relaxation in $\sim 10^8$ years. Many scientific problems can
be addressed only by following the luminous tracers for billions of years.
Testing models of the initial fluctuations by simulating their evolution into
present-day structures is paramount now that COBE has provided a normalization
of the power spectrum on large scales, and we have the correlation functions
of clusters and galaxies on smaller scales. In addition, we have increasing
data on the structure of the universe at earlier epochs. The Hubble Space
Telescope has enabled researchers to follow QSO absorption line systems
to small enough redshifts that galaxy association is unambiguous.
%Dressler and Oemler (private communication)
Researchers are imaging the ``anomalous population of
blue galaxies" in clusters at modest redshift,
%SIRTF and AXAF
and recent investigations 
promise to show us
how clusters of galaxies have evolved. To understand these new discoveries,
we must follow the evolution of the luminous tracers in the general sea of
cosmic structure. 

Common problems emerge in the simulation of large-scale structure, the
formation of a cluster of galaxies, and the formation of a galaxy itself. For
example, the rate of the dissolution of substructure on all three scales has
become a popular way of estimating the present value of the cosmological
density parameter $\Omega$.
%\footnote{$^\dagger$}{A critical value of the
%density parameter $\Omega = 1$ has a flat spatial geometry and separates high
%density, spherically curved Universes ($\Omega > 1$) that will recollapse from
%hyperbolic Universes that expand forever.}.
Carlberg used the merger rate of
galaxies to argue for a value of $\Omega$ near unity.  Richstone, Loeb, and
Turner came to a similar conclusion using the substructure present in clusters
of galaxies. Toth and Ostriker concluded that $\Omega << 1$ by considering the
accretion of satellites by disks. All of these ideas are exciting, but none
are useful without careful calibration against simulations with $10^8$ or
$10^9$ particles. 

Our goals are threefold: (1)
to simulate the formation of a cluster in an appropriate cosmological
context ($\Omega =1$ only), total mass $=10^{16} $ solar masses; 
(2) to simulate the formation of a galaxy in an appropriate cosmological
context, total mass = $10^{13} $ solar masses (3--5 times the mass of the local group);
and (3)
to simulate large-scale structure ($\Omega=1$) and a Cold Dark
Matter power spectrum, total mass $=10^{18} $ solar masses. 
These simulations will each have follow $3 \times 10^7$ particles for 3,000
iterations.
We will run some of these simulations on the KSR
(where we are willing to perform calculations that take weeks)
and also intend to do at least one production run on the
Intel Touchstone DELTA
(where each simulation will take roughly 1 day on the 512-node machine).
%rrosner@mhd5.uchicago.edu Fri Apr 16 10:09:37 1993
%
%
%

%\nopagenumbers
\vspace{.2in}
\centerline{\bf CONVECTIVE TURBULENCE AND MIXING IN ASTROPHYSICS}
\vskip 16pt
\centerline{Robert Rosner}
\centerline{The University of Chicago}
%\centerline{The University of Chicago \& Argonne National Laboratory}
%\centerline{University of Colorado/Boulder}
%\centerline{Michigan State University}
%\centerline{National Center for Atmospheric Research/High Altitude Observatory}
%\centerline{(Goddard Space Flight Center)}
\smallskip
\vskip 16pt

Turbulent convection plays a central role in many astrophysical
circumstances, ranging from the transport of energy from stellar
and planetary interiors to their surfaces, to the mixing
of specific angular momentum and dynamo activity in stars, planets,
and possibly accretion disks, to finally the highly dynamic mixing
encountered in novae and supernovae. We propose to develop the next
generation of multidimensional hydrodynamic codes to attack these
problems.
Specifically, we plan to
use massively parallel machines, establishing
testbed and scalable codes from several approaches. These simulations 
are unique in their simultaneous demands for computing power, data 
handling capabilities, and scientific visualization.
To address these demands, we have organized a team
of researchers from the University of Chicago, Argonne National Laboratory,
the University of Colorado at Boulder, Michigan State University,
and the National Center for Atmospheric Research/High Altitude Observatory.
%ask how Goddard fits in

%
\vspace{.2in}

%\nopagenumbers
\centerline{\bf SCALABLE HIERARCHICAL PARTICLE ALGORITHMS}
\vskip .08886pt
\centerline{\bf FOR GALAXY FORMATION AND ACCRETION ASTROPHYSICS}
\vskip 16pt
\centerline{Wojciech Zurek and M. Warren}
\centerline {Los Alamos National Laboratory}
\smallskip
\vskip 16pt


%From msw@tdo-serv.lanl.gov Tue Apr 27 17:55:27 1993

Particle codes can be applied to a variety of astrophysical (and
other) problems.  We are using a cosmological $N$-body treecode
to study galaxy halo formation caused by
gravitational clustering.
We are also developing
a smooth particle hydrodynamic code
to study tidal disruption of stars in the vicinity
of a black hole.
These codes have been implemented
on parallel supercomputers (especially on the Intel
Touchstone DELTA).
A major challenge is the development of
techniques for analyzing the
tremendous amount of data.

This project is carried out in collaboration with researchers at 
the California Institute of Technology,
Syracuse University,
the Pennsylvania State University,
and Mount Stromlo Observatory.


%From sugar@sarek.physics.ucsb.edu Thu Apr 15 19:03:02 199n{document}

%\nopagenumbers
\vspace{.2in}
\centerline{\bf LATTICE GAUGE THEORY}
\vskip 16pt
%\centerline{\bf THE MIMD LATTICE CALCULATIONS (MILC) COLLABORATON}
\centerline{Bob Sugar}
\centerline {University of California at Santa Barbara}
\smallskip
\vskip 16pt

We plan to carry out a broad program of research in lattice gauge theory. 
Lattice gauge theory addresses some of the most fundamental theoretical
problems in high energy physics and is directly relevant to the
Department of Energy's experimental programs in high energy and nuclear
physics. Our past work has included studies of the mass spectrum of the
strongly interacting particles, the behavior of nuclear matter at high
temperatures, the weak interactions of strongly interacting particles, and 
the Higgs sector of the electroweak interactions. A controlled calculation 
of the spectrum would provide a major test of quantum chromodynamics, 
the theory of the strong interactions. It would also demonstrate that we 
are in a position to make reliable calculations of physical quantities 
that are not as well determined experimentally as the masses. An
understanding of the transition between the state of ordinary
matter and the high-temperature quark-gluon plasma and of the properties of
the plasma is important for the interpretation of heavy-ion collision 
experiments planned by nuclear physicists at facilities 
such as the Relativistic Heavy Ion Collider. It is also important for
cosmological models. The study of nonperturbative effects in the standard model
of high energy physics is likely to be a particularly fruitful area for lattice
gauge theory, as it will bring the theory into
direct contact with ongoing experiments at the major DOE high energy
physics laboratories and with experiments planned at the Superconducting
SuperCollider Laboratory. In particular, lattice gauge theory provides 
the only known avenue for making first-principle calculations of the 
effects of strong interactions on weak processes.  Therefore, it  holds 
the promise of enabling us to make crucial tests of the standard model. 
We propose to continue our work in all of these areas. 
     
The study of lattice gauge theory requires enormous computing resources
and can benefit greatly from the new generation of massively parallel computers.
In anticipation of these high-performance computers,
we have developed parallel code 
that runs on a wide variety of platforms.
At least initially, we plan to use
the Intel Paragon at Oak Ridge National Laboratory, for which the code
has been specifically tuned.
%and has been recognized as one of the grand challenges of computational 
%science. Very significant progress can be made in this field with the 
%new generation of massively parallel supercomputers, such as the Intel
%Paragon.

%From grebogi@chaos.umd.edu Fri Apr 16 10:57:59 1993
%
\vspace{.2in}
\centerline{\bf CONTROLLING CHAOS}
\vskip 16pt
\centerline{ C. Grebogi, E. Ott, and J. A. Yorke}
\centerline {University of Maryland}
%\centerline{{College Park, MD}}
\smallskip
\vskip 16pt


Our research is concerned with using small controlling perturbations
to improve the performance of a system that is in a chaotic state. Two
closely related general problems are being investigated: (1) control
of long-term system behavior by location and stabilization of unstable
periodic and steady-state orbits naturally embedded in the chaotic
attractor, and (2) the rapid direction of an orbit to some target in phase
space.  Our technique for the solution of the first problem can be
implemented without knowledge of a mathematical model of the system.
The technique, which involves the use of embedding methods to analyze experimental data,
has already been carried out in simple experiments by other groups,
including applications to the control of a magnetoelastic buckling
beam, arrhythmically beating heart tissue, lasers, spin-wave systems,
and thermally convecting fluid. Our research currently concentrates on
development of basic techniques for the solution of problems (1) and
(2) for low-dimensional attractors. In this case computational
requirements are not severe. However,
we anticipate that the computational requirements will
rapidly become limiting as we try to move to higher-dimensional chaotic processes.
In particular, we foresee a need for
large memory and rapid search capabilities.

%From crutcher@sirius.astro.uiuc.edu Fri Apr 23 16:54:14 1993

%
%
\vspace{.2in}
%\nopagenumbers
\centerline{\bf RADIO SYNTHESIS IMAGING: AN HPCC APPLICATION}
\vskip 16pt
\centerline{Richard Crutcher and Michael Heath}
\centerline{ National Center for Supercomputing Applications}
\smallskip
\vskip 16pt


This project involves the direct implementation of three computing
recommendations of the Astronomy and Astrophysics Survey Committee
(chartered each decade by the National Academy of Sciences and the
National Research Council to survey needs of astronomy).  The project
will implement a prototype of the next generation of astronomical
telescope systems---remotely located telescopes connected by
high-speed networks to very high performance, scalable architecture
computers and on-line data archives, which are accessed by astronomers
over Gbit/sec networks.  Specifically, a data link will be installed
between the millimeter-wave synthesis array at Hat Creek, California,
operated by the Berkeley-Illinois-Maryland Association (BIMA) and the
National Center for Supercomputing Applications (NCSA) at Urbana,
Illinois, for real-time transmission of data to the high-performance,
massively parallel Thinking Machines Corporation CM-5.  The very
computationally intensive algorithms for calibration and imaging of
radio synthesis array observations will be optimized, and new
algorithms that use the massively parallel architecture will be
developed.
The project will experiment with and develop the
techniques for (1) connecting via HiPPI the MPP CM5 and a 
high-performance visualization workstation (the SGI ONYX) at NCSA for
distributed high-performance computing and visualization, and (2)
extending this distributed computing and visualization environment by
connecting a high performance visualization workstation at the
University of Wisconsin to the NCSA CM5 over the BLANCA gigabit/sec
network.  Finally, the project will implement an on-line, digital
archive of the BIMA radio telescope database and a digital library of
processed images.

This research and development project will be
carried out by a team of experts in radio astronomy, algorithm
development for massively parallel architectures, high-speed
networking, and database management in conjunction with Thinking Machines Corporation
personnel.  The development of this complete software, 
distributed computing, and data archive and library solution to the radio
astronomy computing problem will advance our expertise in 
high-performance computing and communications technology and our knowledge
of the
astronomical universe.


\vspace{.2in}
\begin{flushleft}
{\large \bf Panel C -- Computational Biology, Chemistry, and}\\
{\large \bf Material Sciences}
\end{flushleft}
\hspace{.2in}Chair: {\it  Bob Martino, NIH }


\vspace{.2in}
\centerline{\bf HIGH-CAPACITY ATOMIC-LEVEL SIMULATIONS FOR }
\centerline{\bf DESIGN OF MATERIALS}
\vskip 16 pt
\centerline{William Goddard}
\centerline{Caltech}
\smallskip
\vskip 16pt

We are working on two projects: one in quantum chemistry, the other in molecular
dynamics.
In the quantum chemistry project, we are concerned with
systems of hundreds to thousands of atoms.
The pseudo-spectral approach is being used, as well as Hartree-Fock.
The goal is to build an algorithm that will scale as $N^2$.
in the molecular dynamics project, we are dealing with 200,000 to 1 million
atoms per cell.
Our approach is to divide the system into a hierarchy of cells.
On a 30-node KSR machine, this approach turns out to be both faster and more accurate than
standard approaches.
We are now converting to the CM-5 and studying ways to exploit distributed memory.

\newpage
%\vspace{.2in}
\centerline{\bf COMPUTATIONAL QUANTUM MATERIALS:}
\vskip .08886 truein
\centerline{\bf FIRST PRINCIPLES SIMULATION OF MATERIALS PROPERTIES}
\vskip 16pt
\centerline{ G. M. Stocks, B. N. Harmon, and J. W. Davenport}
\centerline {Oak Ridge National Laboratory, Ames Laboratory, and}
\centerline{Brookhaven National Laboratory}
%\centerline{{Oak Ridge, TN 37831-6373}}
\smallskip
\vskip 16pt

We combine developments in the first principles theory of the electronic
structure and energetics of materials, atomistic molecular dynamics
simulations, and parallel processing to solve materials science problems
of scientific and technological importance. Specifically, we are
developing a range of methods for materials properties
simulation that are based on a quantum mechanical description of the
interactions.
The methods include tight binding molecular dynamics,
Car-Parrinello molecular dynamics, and locally self-consistent
multiple scattering theory.
These methods bridge the gap between
traditional molecular dynamics and Monte Carlo simulation methods based
on classical interactions and modern first principles electronic structure
methods based on density functional theory.
The new methods will enable us to perform
quantum-statistical simulations on systems containing hundreds to thousands
of atoms.
We use parallel processing
techniques in order to develop efficient scalable techniques that will
allow us to solve materials problems
currently out of reach.

\vspace{.2in}
\centerline{\bf COMPUTER-AIDED DESIGN OF BIOLOGICAL}
\vskip .08886 truein
\centerline{\bf AND  BIOMIMETIC MATERIALS:}
\vskip .08886 truein
\centerline{\bf COMPUTATIONAL DESIGN FOR CATALYSIS}
\vskip 16pt
\centerline{W. J. Camp, J. S. Nelson, S. J. Plimpton, and M. P. Sears}
\centerline { Sandia National Laboratories}
\smallskip
Our Grand Challenge project is the creation of massively parallel
computational tools enabling the study of catalytic and biocatalytic
reactions
from quantum mechanical and atomistic viewpoints.  The application
areas include microbiology (enzymatic reactions in proteins) and
energy (gasoline cracking via polymeric reactions, catalytic
reactions
on crystal and cluster surfaces).  We are developing simulation codes
that
perform quantum mechanical electronic structure calculations via
local density functional, Hartree-Fock,
and
many-body formulations and molecular dynamic and Monte Carlo
computations using empirical force fields.  Most of the work is
with codes we are writing ourselves; in other cases we are
parallelizing existing commercial codes (DMOL and Discover from
Biosym).

Typical computations are diffusion of small polymers in zeolites,
metalloporphyr
in trapping and reduction of carbon dioxide,
active site analysis of metal clusters, and  charge density shifts
in enzyme and cofactor reactions.
More algorithm development is needed to carry out these computations
effectively.
For example,
longer
timescale molecular dynamics simulations will need different algorithms
that allow
for much longer timesteps.
Currently, performance is limited by the speed of individual
processors.
We are exploring implicit methods and bond-constrained techniques to
overcome this barrier.
Similarly, new serial and parallel algorithms are needed to improve
the
scaling characteristics of electronic structure calculations, to
enable larger atomic systems to be studied.
We are exploring two promising linear scaling methods:
one new algorithm is being explored that exhibits explicitly linear scaling (i.e.,
the computational work to simulate $N$ atome is $A \cdot N$, with $A$
being a modest-sized number).
In another project we are developing a massively parallel version of Yang's
divide-and-conquer algorithm which also scales linearly.

In conjunction with the Grand Challenge, an optimized parallel library of dense-matrix
linear algebra and finite-difference decomposition routines has been developed.
The library includes MIMD routines with C and F77 interfaces for
finite-difference operations, FFTs,
many BLAS Level 1 and 2 operations, matrix transpose
and multiply, Cholesky and LU decompositions, and Hermitian eigenvalue problems,
as well as parallel graphics.

\vspace{.2in}
%\nopagenumbers
\centerline{\bf HIGH-PERFORMANCE COMPUTATIONAL STRUCTURAL BIOLOGY}
\vskip 16pt
\centerline{ Jerry E. Solomon}
\centerline { California Institute of Technology}
%\centerline{{Oak Ridge, TN 37831-6373}}
\smallskip
\vskip 16pt


The Computational Structural Biology project is focused on two specific
science problems:
(1) de novo protein three-dimensional structure prediction from primary amino
acid sequence; and (2) predictive computational models of cellular organelles.
Both of these problems,
in addition to being computationally intensive, share the
necessity of building new programming and user tools for exploring the full
details of the problem.

We have developed a hierarchical computational approach
to the de novo prediction problem which utilizes a novel static Monte Carlo
method for generating large ensembles of candidate protein backbone
structures on
%what is fcc
an fcc lattice as the top-level (coarse-grain) step.
Our lattice model is
capable of naturally incorporating known protein folding constraints into the
chain
generation process, and the algorithm is easily parallelized for
use on large-scale
massively parallel processors (MPPs).
The lattice model, although a drastic simplification of the real
conformational space of proteins,
is able to capture much of the fundamental physics
and chemistry of the folding process, including solvent accessibility effects.
We
illustrate the algorithm and some early results used for validation
on serial workstations.  The predictive computational model problem for
cellular organelles
focuses on building accurate (testable) models of the flagellar
axoneme and the tRNA transcription operations of the ribosome.
These models utilize the so-called
teleological approach, wherein the problem is abstracted in terms
of the system behavior rather than explicitly in terms of the problem
constraints.

On the
HPCC technology development side, we are concentrating on building
sophisticated tools
that enable both applications programmers and science users to take
full advantage
of the computational power afforded by large-scale MPPs to explore alternative
approaches to problem solutions.  In particular,
we are attempting to build what we term a ``numerical laboratory,"
which allows users to literally conduct experiments
much in the same way as an experimental biologist would
carry out experiments in
a wet lab.  These tools, implemented in CC++ (a parallel,
object-oriented language)
developed at Caltech,
are being used to construct a protein-folding workbench and
a predictive computational modeling workbench. 
Both of these workbenches are capable
of operating at three distinct levels,
depending upon the computer literacy of the
user, and are designed to be easily extensible in functional capability.




\vspace{.2in}
%
%\nopagenumbers
\centerline{\bf HIGH-PERFORMANCE COMPUTATIONAL CHEMISTRY:}
\vskip .08886 truein
\centerline{\bf SCALABLE ALGORITHMS FOR GRAND CHALLENGE APPLICATIONS}
\vskip 16pt
\centerline{ T. H. Dunning, Jr.,  and A. Wagner}
\centerline{ Pacific Northwest Laboratory and Argonne National Laboratory}
\smallskip
\vskip 16pt

The High-Performance Computational Chemistry project is a multilaboratory 
collaboration to develop the computational technology required to solve Grand 
Challenge-class problems in computational chemistry.  This focus of this 
project is on the development of the new algorithms, software, and diagnostics 
that will be required to fully exploit massively parallel computers capable of 
teraflops performance.  In particular, efforts center on the 
development of algorithms that scale to larger numbers of processors, use 
hierarchical memory much more effectively, and make much smaller I/O demands 
than current methods.  The goal is to develop modeling systems that are capable 
of solving problems orders of magnitude larger than currently possible.  The 
work is focused on a suite of computational chemistry modeling methods 
appropriate to three specific problem areas of urgent interest to the U.S. 
Department of Energy and the chemical and petroleum industries:  the 
incineration of halohydrocarbons, the interaction of soil minerals with 
pollutants, and the redesign of biodegradative enzymes.

Work is 
under way on the development of performance models and code for a fully 
distributed Hartree-Fock program for computing the electronic structure of 
molecules, scalable methods for dense eigensystem solution, and improved data 
sharing and data communications paradigms for computational chemistry codes.  
In the future this work will be extended to include scalable techniques for 
treating electron correlation (both perturbation theory and configuration 
interaction techniques) and for coupled semi-empirical electronic structure-
molecular dynamics. 

The High Performance Computational Chemistry project is a 
collaboration involving the Pacific Northwest Laboratory and Argonne National 
Laboratory, with participation by five industrial collaborators (Allied Signal, 
Amoco, DuPont, Exxon and Phillips).


%
\vspace{.2in}
%\nopagenumbers
\centerline{\bf HIGH-PERFORMANCE COMPUTING IN STRUCTURAL BIOLOGY}
\vskip .08886 truein
\centerline{\bf AND MEDICAL IMAGING}
\vskip 16pt
\centerline{ Robert L. Martino}
\centerline { National Institutes of Health}
%\centerline{{Oak Ridge, TN 37831-6373}}
\smallskip
\vskip 16pt

%Computational Bioscience and Engineering Laboratory
Division of Computer
%Research and Technology

Scalable parallel computer architectures provide the computational rates
demanded by advanced biomedical computing problems. NIH has developed a
number of parallel algorithms and techniques useful in determining
biological structure and function. These applications include processing
of electron micrographs to determine the three-dimensional structure of
viruses, using ab initio quantum mechanical methods to investigate the
energetics of how anti-cancer drugs affect the DNA in cancer cells,
applying molecular dynamics to study the affects of hydration on protein
structure, calculating the solvent accessible surface area of proteins
to predict the three-dimensional conformation of these molecules from
their primary structure, and aligning two three-dimensional positron
emission tomography images for brain function studies. Timing
results demonstrate substantial performance improvements with parallel
implementations compared with conventional sequential systems.


\vspace{.2in}
\centerline{\bf HIGH-PERFORMANCE IMAGING IN BIOLOGICAL RESEARCH}
\vskip 16pt
\centerline{D. Lansing Taylor and Scott E. Fahlman}
\centerline{Carnegie Mellon University}
\smallskip
\vskip 16pt


The goal of the Automated  Interactive Microscope project  is to
bring high-performance computing to bear on the image processing,
analysis, storage, and display problems
associated
with advanced fluorescence microscopy in the biological sciences.
We will investigate
a wide variety of image processing, display, and recognition algorithms.
For example, we will perform
nearest neighbor and minimum entropy deconvolutions to extract a clear
three-dimensional
image from a set of image planes at different depths in the specimen.
Part of our research will focus on 
determining which algorithms are most useful.
We plan to use
graphics workstations (e.g., the SGI Onyx/2 with VTX graphics attached
to the microscope) and, via 
a high-speed link, run
on the CRAY C90, T3D, and CM-2 at the Pittsburgh Supercomputing
Center. 

We believe that this research will
change the practice of microscopy in at least four important ways:
(1) we will be able to run current image-processing and display algorithms
in real time, providing timely feedback for the experimenter;
(2) we will be able to employ much more ambitious processing and display
algorithms than are used currently, allowing the experimenter to see the
data in new ways;
(3) we will be able to automate some of the recognition tasks associated
with biological experiments, making it possible for the system itself to
detect and record interesting events, where "interesting" is defined by the
experimenter; and  
(4) once the system can recognize interesting events, it can perform
certain experimental interventions automatically.

%From jerry@maxjr.hood.caltech.edu Sat Apr 24 15:55:39 1993





%From gms@gmsis.ms.ornl.gov Thu Apr 22 07:51:50 1993
%
%
%
%
%
\vspace{.2in}

%\n
%\nopagenumbers

\vspace{.2in}
\centerline{\bf COMPUTATIONAL QUANTUM MATERIALS:}
\vskip .08886 truein
\centerline{\bf QUANTUM STRUCTURE OF MATTER}
\vskip 16pt
\centerline{T. Barnes, V. Overacker, M. R. Strayer, S. Umar}
\centerline {Oak Ridge National Laboratory, Vanderbilt University}
%\centerline{{Oak Ridge, TN 37831-6373}}
\smallskip
\vskip 16pt
This proposal centers on three computational methods to apply classical and quantum field theory to atomic, nuclear, and condensed matter physics.
These are (1) the time-dependent Hartree-Fock method applied to
collisions and structure of atoms, molecules, and nuclei;
(2) Monte Carlo perturbation theory applied to processes in 
accelerator and detector physics, notably RHIC;
and (3) hydrodynamics applied to relativistic nuclear collisions and plasmas.
Our objective is to advance the study of matter under strongly interacting conditions,
as encountered in many branches of energy research,
including fusion plasmas, lasers, and accelerators.
While many of the proposed methods are transferable into applied areas,
our emphasis is on understanding systems simple enough that results of high
reliability are possible.
Part of our proposed research
deals with matter at energies below one MeV---an area that is
also relevant to quantum chemistry and
materials research.
The remainder of our investigations
deal with matter at higher energies---an area unique to the Grand Challenges.
%not clear
%From bill@cs.sandia.gov Tue Apr 20 15:15:27 1993
%


%From mecolv@california.sandia.gov Fri Apr 23 18:28:35 1993


\vspace{.2in}
%\newpage
\centerline{\bf
	 COMPUTER-AIDED DESIGN OF BIOLOGICAL}
\vskip .08886 truein
\centerline{\bf AND BIOMIMETIC MATERIALS:}
\vskip .08886 truein
\centerline{\bf PHARMACEUTICALS AND AGROCHEMICALS}
\vskip 16pt
\centerline{ Mike Colvin}
\centerline { Sandia National Laboratories}
\smallskip
\vskip 16pt

The U.S. pharmaceutical and agricultural chemical
industries are unequaled in the world.  Not only do U.S. firms hold the
largest share of the worldwide market, they are the source of most
innovative new products in the biomedical and agrochemical markets.
Both industries differ from most manufacturing
industries in that a much larger fraction of their revenue goes into
research and development of products for which they have only a few
years of exclusive license protection.  For example, a typical
pharmaceutical drug takes 12 years to develop and get approved,
leaving only 5 years of patent protection in which to recoup
hundreds of millions of dollars in development costs.  Hence, there is
an enormous competitive advantage in shortening this development time.
Moreover, traditional, exhaustive screening methods of designing new
drugs and pesticides are proving inadequate.  Instead,
``rational" methods are being sought to design pharmaceuticals
and pesticides with predictable potencies.

The Center for
Computational Engineering at Sandia is collaborating with U.S. industry and
universities to use new computational tools to solve problems is
biomedical and agricultural chemistry.
Sandia has unique experience in using highly accurate ab
initio quantum chemical methods for large chemical systems.  These
very methods are far more computationally intensive than 
traditional semi-empirical methods and require extensive development of new
algorithms and implementation on massively parallel supercomputers.
In addition, Sandia has extensive capabilities in computational
modeling of materials and molecular dynamics simulations.

Sandia is collaborating with researchers at a major U.S.
pharmaceutical and several universities and medical schools to apply
computational chemistry to solve problems in pharmaceutical and
agrochemical design.  The collaboration with the pharmaceutical
company has involved computer modeling of numerous pharmaceuticals
including anti-viral drugs and diagnostic imaging agents.  Sandia is
also working with the Johns Hopkins Medical School to develop more
potent analogs of the widely used anticancer drug cyclophosphamide.
Sandia performed a series of ab initio quantum chemical calculations
of cyclophosphamide metabolites bound to DNA, which constituted some
of the largest quantum chemical calculations ever performed.  Another
collaboration is with the Department of Entomology at the University
of California at Davis to help develop a new type of highly potent and
environmentally save insecticide.  Sandia is also collaborating  with New
York University and Lawrence Livermore National Laboratory to study
how environmental carcinogens bind to DNA.




\vspace{.2in}
%\newpage
%\vspace{.2in}
\centerline{\bf TEXAS CENTER FOR ADVANCED MOLECULAR COMPUTATION}
\vskip 16pt
\centerline{
Roland Glowinski, J. Andrew McCammon,  B. Montgomery Pettitt,}
\centerline{
and L. Ridgway Scott}
\centerline{University  of Houston}
%\centerline{{Oak Ridge, TN 37831-6373}}
\smallskip
\vskip 16pt
High-performance computing can solve critical problems in
biomolecular design, once several issues are resolved.
The most obvious problem
relates to the difficulty in using advanced computer architectures.
In addition, new algorithms (mathematical and computer
science-based) and methods (physical science-based) are needed
to solve problems with reasonable efficiency. Finally,
to guide
and provoke the development of both algorithms and software,
scientists need a
detailed understanding of what
types of problems can be treated with vastly improved computing
resources.
We address all of these research areas in this proposal.

We will utilize and enhance parallel programming languages developed
both by our group and by others to facilitate development and maintenance
of complex algorithms for diverse high-performance architectures.
One goal is to achieve
supercomputer performance (and better) on low-cost parallel computers for
commercial-grade codes with minimal programmer effort.

Examples of algorithms that we will study include
fast summation methods for nonbonded forces in molecular
dynamics,
more efficient (implicit) time stepping schemes in
molecular dynamics (which preserve energy),
the use of multigrid and domain-decomposition
techniques to resolve the electrostatic force
distributions in the Brownian dynamics model, and
parallel algorithms and data structures with an
emphasis on load balancing.

The following Grand Challenge problems will be studied to
improve our understanding of what can be resolved computationally (and
what cannot):
(1) thermodynamic cycle perturbation theory applied to
ligand-binding proteins,
(2) Poisson-Boltzmann/Brownian dynamics applied to protein design,
(3) non-Boltzmann sampling and transition-state trajectories of
ligand binding,
(4) quenched high-temperature dynamic peptide mimetic design, and
(5) grand canonical dynamics applied to salt solutions of
nucleic acid triple helices.
\vspace{.2in}
%From martino@alw.nih.gov Thu Apr 22 10:25:07 1993

\newpage
%\vspace{.2in}

\begin{flushleft}
{\large \bf Panel D -- Computational Fluid and Plasma Dynamics} 
\end{flushleft}
\hspace{.2in}Chair: {\it Lee Holcomb, NASA}

\vspace{.2in}

\centerline{\bf HIGH-PERFORMANCE COMPUTATIONAL METHODS FOR}
\vskip .08886 truein
\centerline{\bf COUPLED FIELDS AND GAFD TURBULENCE}
\vskip 16pt
\centerline{Carlos Felippa and Juri Toomre}
\centerline {University of Colorado at Boulder}
%\centerline{{Boulder, CO  80303}}
\smallskip
\vskip 16pt

This project represents a merger of two HPCC Grand Challenge proposals:
``High-Performance Computational Methods for Coupled Field Problems'' 
and ``Coherent Structures and Dynamics of Geophysical and Astrophysical
Turbulent Flows.''  The merged project involves a team of researchers at the
University of Colorado at Boulder, the University of Colorado at Denver, the
University of Minnesota, and the National Center for Atmospheric 
Research.  This team will (1) develop and implement algorithms and 
software on massively parallel computers for solving coupled field problems in
structural and fluid dynamics and (2) study turbulent flows that arise in
geophysical and astrophysical fluid dynamics (GAFD). 

The first component considers three problems: three-dimensional
aeroelasticity, massively actuated ``adaptive'' structures, and
electromagnetic-thermomechanical interaction.  The emphasis in this component is
on investigating benefits of finite-element-based partitioned analysis methods and
heterogeneous parallel computation.

The second component considers
four problems: geostrophic turbulence, oceanic convection, deep
convection in planetary atmospheres, and stellar compressible convection,
all constrained by effects of rotation and stratification. 
The emphasis in this component is on exploiting
massively parallel architectures to increase the resolution of
in three-dimensional simulations employing
%what are PPM
(variously) pseudo-spectral, finite-difference, multigrid, and PPM approaches in
studying the intense turbulence encountered in planetary and stellar settings,
including the large-scale coherent structures and mean flows that can coexist
with such turbulence.

Computer science challenges paced by these problems
include performance modeling and analysis, performance evaluation tools,
program transformation and automatic data distribution, heterogeneous
computation, very large scientific databases, and high-performance
visualization.





%
%
%
\vspace{.2in}
%

\centerline{\bf MULTIDISCIPLINE SIMULATION OF HIGH-SPEED CIVIL TRANSPORT}
\vskip 16pt
\centerline{M. D. Salas}
\centerline { NASA/Langley Research Center}
\smallskip
\vskip 16pt



In the past two decades, a foundation has been established for the advanced 
design of aerospace vehicles in mathematics, computer software and hardware,
and engineering practices, leading to a new emerging technology called 
multidisciplinary design and optimization (MDO). This technology requires the
repetitive analysis of the vehicle structure, flow field, propulsion system,
and controls as design variables are changed in the search for an optimum. The
number of analyses required is proportional to the number of design variables
that are allowed to change. For a typical high-speed civil transport 
configuration, the number of design variables can reach several hundreds. Since
each analysis could take several hours of CPU time on a CRAY Y-MP computer and
could require a Gigaword or more of memory, this Grand Challenge imposes severe
requirements on computer resources. The difference in requirements imposed by the
various disciplines lends itself to a heterogeneous computer implementation.
In addition, the problem requires access to large, dynamic databases from 
different computer architectures and the rapid interrogation and visualization of 
this data.
We explore the computer requirements both in terms of 
hardware and software needed to develop a framework for MDO.


%From chawla@nas.nasa.gov Thu Apr 15 16:24:00 1993

%
%
%
%
%

%\nopagenumbers
\vspace{.2in}
\centerline{\bf MULTIDISCIPLINE SIMULATION OF HIGH-PERFORMANCE AIRCRAFT }
\vskip 16pt
\centerline{K. Chawla}
%\centerline {Research Scientist, MCAT Institute}
\centerline{{NASA/Ames Research Center}}
%\centerline{{Moffett Field, CA}}
\smallskip
\vskip 16pt

This project involves comparing the
performance of high-performance aircraft computational fluid dynamics
simulations
on the parallel machines Intel iPSC-860 and TMC CM-2 and on
the vector machine CRAY Y-MP. The problems under consideration require
the use of multizone overset grid topology and solution of the Navier-Stokes
equations. Our focus is on issues such as
performance degradation on the Intel resulting from heavy message passing while
solving all grids in parallel,
strategy to map grids to processors, and  disadvantages arising
because of the SIMD nature of the CM-2.

We have compared the performance of various matrix solvers
on 
different parallel machines,
focusing on a single discipline application---specifically, the simulation of flow past a 
delta wing
with thrust reverser jets in ground effect.
We are also exploring 
strategies to map multidiscipline
problems to the parallel machines, in particular, the Intel.




%From salas@tab00.larc.nasa.gov Tue Apr 20 10:02:38 1993
%
%
%
%
%
%\nopagenumbers

\vspace{.2in}
\centerline{\bf MULTIDISCIPLINE SIMULATION OF PROPULSION SYSTEMS}
\vskip 16pt
\centerline{ Russell W. Claus}
\centerline { NASA Lewis Research Center}
%Interdisciplinary Technology Office
%Cleveland, OH 44135 
\smallskip
\vskip 16pt

Implementing new technology in aerospace propulsion systems is becoming
prohibitively expensive.  One of the major contributors to the high cost is the
need to perform many large-scale system tests. The traditional design analysis
procedure decomposes the engine into isolated components and focuses attention
on each single physical discipline (e.g., fluid  or structural dynamics).
Consequently, the interactions that naturally occur between components and
disciplines can be masked by the limited interactions that occur between
individuals or teams doing the design and must be uncovered during expensive
engine testing.  This situation can pose serious problems for a highly integrated
propulsion system, where tight coupling can produce unforeseen interactions
having adverse effects on system performance.  For example, the installation of
an improved efficiency turbine into an existing engine can result in a
rebalancing of the engine cycle, with the consequence that the compressor is
driven at a less efficient operating point, thereby reducing the total system
performance as measured by specific fuel consumption or some other system
parameter. As a result, the introduction of advanced technology into propulsion
systems takes many years and is prohibitively expensive. New design techniques
that combine concurrent engineering and numerical simulation may provide a
system design solution.

We discuss a cooperative effort of NASA, industry, and
universities to integrate disciplines, components, and high-performance
computing into a Numerical Propulsion System Simulator (NPSS). NPSS is involved
in the development of propulsion models for the computational aerosciences
Grand Challenges. Development of propulsion modules requires advances in
the following technologies: (1) interdisciplinary analysis to couple the
relevant disciplines such as aerodynamics, structures, heat transfer,
chemistry, materials, controls; (2) integrated system analysis to couple d
subsystems, and components at an appropriate level of detail; (3) a 
high-performance computing platform composed of a variety of architectures,
including massively parallel processors, to provide the required computing
speed and memory; and (4) a simulation environment that provides a user-friendly
interface between the analyst and the multitude of complex codes and computing
systems that will be required to perform the simulations.


\vspace{.2in}
\centerline{\bf COMPUTATIONAL FLUID AND COMBUSTION DYNAMICS }
\vskip 16pt
\centerline{Phil Colella}
\centerline{University of California - Berkeley}
\smallskip
\vskip 16pt

We are studying
computational fluid dynamics as applied to combustion problems.
Specifically, our focus is on three-dimensional, time-dependent, turbulent
fluid-flow problems,
in a variety of Mach number regimes ranging from extremely subsonic
to hypersonic, and in complex physical geometries.
Our numerical methods are based
on finite difference methods on block---structured,
dynamically adaptive grids in which the total number of grid points
and their distribution in space change as function of time and the solution.
We also use two techniques for representing complicated
boundary geometries: composite overlapping meshes (e.g., CMPGRD,
Chimera), and Cartesian grid/volume-of-fluid representation
of geometry.
We exploit visualization---in particular, animations
generated directly from a massively parallel processing system---to
gain new insights into the phenomena.
To date, our
principal limiting factor has been the capability to run very
large simulations. 
MPP systems promise to provide the necessary resources to address
truly Grand Challenge-scale problems.





\newpage
%

%From dbarnes@ctrss2.lanl.gov Mon Apr 19 15:28:47 1993

%
%
%
\centerline{\bf NUMERICAL TOKAMAK PROJECT}
\vskip .08886 truein
\centerline{Dan Barnes}
\centerline{Los Alamos National Laboratory}
\smallskip
\vskip 16pt


The Numerical Tokamak Project (NTP) is applying high-performance computing and communication to the most elusive aspect of tokamak design and operation---the problem of
losses of plasma particles and energy.
These losses always exceed the rates
calculated for a completely static plasma because of fluctuations in which a
large number of the confined particles move together (collectively).  Such
collective fluctuations enhance transport of heat in much the same way that
fluid fluctuations (turbulence) lead to enhanced heat transport rates, for
example, because of waves in the ocean or eddies in the atmosphere.  The
understanding of this process and its scaling as machine parameters are
increased is critical to tokamak development.  While
impressive progress has been made, a key difficulty that remains is the 
detailed calculation of the solution of plasma transport models.  The recent
increase in the computing power available from massively parallel computers has 
made feasible the direct numerical solution of these models.

Unique physical features of this problem include the representation of
the toroidal geometry, in which magnetic field lines travel on nested tori
with a varying winding number, nonlocal response associated with the almost
free motion of plasma particles along this magnetic field, 
nonlocal particle interactions via the electromagnetic field,
and removal of fast
time scales and/or small space scales by analytic or numerical approaches.
The particular numerical difficulties that are introduced include use of 
general coordinates with nonstandard periodicity conditions, nonlocal 
dependencies associated with the solution of nonconstant coefficient elliptic
problems, and nonlocal mapping of particle lists onto grid lists.

The NTP has developed both fluid and particle (kinetic) approaches for 
the study
of tokamak turbulent transport.  The former offers the advantages of no
numerical noise and relatively few degrees of freedom per physical mesh point.
The latter faithfully represents the long-mean-free path effects in a
much more computationally intensive approach, and introduces the additional
complication of sampling noise.  Typical fluid calculations include about 
half a million degrees of freedom, while typical particle calculations use one
to several million particles.

Unique numerical techniques developed to deal with the NTP problem include
domain decomposition as a means of partial localization of particle 
information, fast parallel FFTs, convolutions,  and linear system solvers,
and conjugate gradient elliptic solvers.  These approaches are described
in terms of their numerical implementations on various (massively) parallel
platforms (CRAY, TMC, and Intel).  Network and visualization requirements to
support these algorithms and their applications are also described.


\newpage
%\vspace{.2in}
\begin{flushleft}
{\large \bf Panel E -- Applications of Artificial Intelligence} 
\end{flushleft}
\hspace{.2in}Chair: {\it Bob Voigt, NSF}

\vspace{.2in}
\centerline{\bf HIGH-PERFORMANCE COMPUTING FOR LEARNING:}
\vskip .08886 truein
\centerline{\bf SUPER-HUMAN SUPERCOMPUTING vs SUPER-COMPUTING}
\centerline{\bf FOR HUMAN COMPUTATIONS}
\vskip 16pt
\centerline{Robert C. Berwick and Tomaso Poggio}
\centerline{Massachusetts Institute of Technology} 
%Center for Biological and Computational Learning
%Department of Brain and Cognitive Sciences
\smallskip
\vskip 16pt

Many supercomputer problems involve important scientific
calculations carried out at superhuman speeds: physical
simulation of molecular structure through quantum mathematics,
hydrodynamics, weather and atmospheric prediction, and many others.
Another set of Grand Challenge problems underscores a rather
different kind of high-performance calculations: those carried
out by the human brain, especially involving learning and
perception.
%such as face recognition, motor control, and speech.
From this perspective, these human-like problems stretch the
limits of sample and memory complexity, rather than the
computational complexity traditionally linked to supercomputing.
This distinction illuminates once again the difference between
super-human supercomputing and super-computing for human
computations.

By envisioning
a simple conversation with two people,
we illustrate the pervasiveness of
memory-intensive learning and brain-like supercomputing in
everyday human tasks, what computational progress has been made
and what new computational demands are entailed, and how this
work points the way to new supercomputer architectures suited to
nonscientific, human-oriented Grand Challenge problems.


%From muntz@CS.UCLA.EDU Thu Apr 15 18:13:19 1993

%
%

%
\vspace{.2in}


%\nopagenumbers
\centerline{\bf DATA ANALYSIS AND KNOWLDEGE DISCOVERY}
\vskip .08886 truein
\centerline{\bf IN GEOPHYSICAL DATABASES}
\vskip 16pt
\centerline{Richard Muntz; Presenter Carlo Zaniolo}
\centerline{University of California - Los Angeles}
\smallskip
\vskip 16pt

%Team: Richard Muntz, Josef Skrzypek, Carlo Zaniolo   [UCLA Computer Science Dept.]
%Carlos R. Mechoso  [UCLA, Department of Atmospheric Sciences] Leon Alkalaj, Daniel McCleese [JPL]

 
This project will demonstrate the technical feasibility of building
sophisticated information systems for geophysical databases
and the benefits of such systems in promoting both  
theoretical and experimental research in the earth sciences.
The testbed includes atmospheric model data,
satellite stratospheric data,
and climate data. Characteristic
of these applications are the identification
and monitoring of complex patterns and their
evolution over space and time.
We will study novel
indexing and abstraction techniques for efficient
search and monitoring of massive data sets,
and the
optimization of complex spatial-temporal queries
and rules. An implementation based on a mass-storage
system and an intelligent front-end 
is planned. Supercomputer testbeds will be used
for parallel search and computation intensive functions.


\chapter{Workshop Invitees}

\begin{flushleft}
Robert Allen, AT\&T\\
Lou Auslander, CUNY\\
Dave Bader, DOE Office of Health and Environmental Research\\
Mike Bailey, San Diego Supercomputer Center\\
Ron Bailey, NASA Ames Research Center\\
Raymond Bair, Pacific Northwest Laboratory\\
Dan Barnes, Los Alamos National Laboratory\\
Thomas Barnwell, Environmental Protection Agency\\
Alan Barr, Caltech\\
Forest Baskett, Silicon Graphics Corp.\\
Isabel Beichl, National Institute of Standards \& Technology\\
John Bell, DOE\\
Marsha Berger, Courant Institute\\
Donna Bergmark, Cornell University\\
Fran Berman, University of California - San Diego\\
Robert C. Berwick, Massachusetts Institute of Technology\\
Kenneth Birman, Cornell University\\
Alan Blatecky, MCNC\\
Marjorie Blumenthal, National Academy of Sciences/Nat. Research Council\\
Ronald Boisvert, NIST\\
Jay Boris, Naval Research Laboratory\\
Lou Branscomb, John F. Kennedy School for Government\\
Joseph Bredekamp, NASA Headquarters\\
Alfred Brenner, Supercomputing Research Center\\
Frederick Brooks, Jr., University of North Carolina\\
Robert Brown, Silicon Graphics, Inc.\\
J. C. Browne, University of Texas\\
Duncan Buell, Supercomputing Research Center\\
Henry Burkhardt, Kendall Square Research Corp.\\
Ralph Burnstein, Electrical Power Research Institute\\
James Burrows, National Institute of Standards \& Technology\\
DaeWon Byun, Environmental Protection Agency\\
Bill Camp, Sandia National Laboratories - Albuquerque)\\
David Cargo, National Security Agency\\
John Carlson, Cray Research, Inc.\\
William Carlson, Supercomputing Research Center\\
Pat Carson, National Institutes of Health\\
T. L. Casavant, University of Iowa/Parallel Processing Lab.\\
Charlie Catlett, University of Illinois NCSA\\
John Cavallini, Department of Energy\\
Kalpana Chawla, NASA Ames Research Center\\
Y. T. Chien, National Science Foundation\\
Alok Choudhary, Syracuse University\\
Mel Ciment, National Science Foundation\\
Charles Clark, NIST\\
Russel Claus, NASA Lewis Research Center\\
Carlie Coats, North Carolina Supercomputing Center\\
Tom Cole, U.S. Army Engineer Waterways Experiment Station\\
Phillip Colella, University of California - Berkeley\\
Stephen Colley, Ncube\\
Michael Colvin, DOE\\
George Cotter, National Security Agency\\
Thomas Crocket, NASA Langley Research Center\\
Richard Crutcher, University of Illinois\\
Ernie Daddio, Environmental Information Service\\
James Davenport, Brookhaven National Laboratory\\
Anil Deane, NASA Goddard Space Flight Center\\
Viktor Decyk, Jet Propulsion Laboratory\\
Tom DeFanti, UIC/EECS\\
Robin Dennis, Environmental Protection Agency\\
Carleton DeTar, University of Tennessee\\
John Dennis, Rice University\\
Dave DeWitt, University of Wisconsin\\
Charles Dickens, OSTP\\
Pete Dillingham, Cray Research Inc.\\
Jack Dongarra, University of Tennessee\\
John Dorband, NASA Goddard Space Flight Center\\
Shawn Downing, NOAA\\
Richard Draper, Supercomputing Research Center\\
Kevin Drogenmeir, University of Oklahoma\\
Thom Dunning, Pacific Northwest Laboratory\\
Doug Dwoyer, NASA Langley Research Center\\
Roger Dyson, NASA Lewis Research Center\\
Todd Elvins, San Diego Supercomputer Center\\
Robert Ewald, Cray Research, Inc.\\
Richard Ewing, Texas A\&M University\\
Carlos Felippa, University of Colorado at Boulder\\
Jack Fellows, DOE Office of Management and Budget\\
Jeanne Ferrante, University of Colorado\\
Robert Ferraro, Jet Propulsion Laboratory\\
James Fischer, NASA Goddard Space Flight Center\\
Jon Flower, ParaSoft Corp.\\
Greg Follen, NASA Lewis Research Center\\
Ian Foster, Argonne National Laboratory\\
Geoffrey Fox, Syracuse University\\
Joan Francioni, University of Southwestern Louisiana\\
Richard Freund, NCCOSC RDTE\\
Richard Friesner, Columbia University\\
Kenneth Galluppi, North Carolina Supercomputer Center\\
Dennis Gannon, Indiana University\\
John H. Gardner, Naval Research Laboratory\\
Al Geist, Oak Ridge National Laboratory\\
Louis Gerstner, IBM Corporation\\
John H. Gibbons, Office of Science and Technology Policy\\
Norman Glick, National Security Agency\\
William A. Goddard, California Institute of Technology\\
Robert Goldberg, Government Operations\\
Mel Goldstein, NASA Goddard Space Flight Center\\
Gene Golub, Stanford University\\
Howard Gordon, Supercomputing Research Center\\
Al Gore, White House\\
David Gottlieb, Brown University\\
Steven Gottlieb, Indiana University\\
Leslie Greengard, Courant Institute, New York University\\
Steve Griffin, National Science Foundation\\
Nico Habermann, National Science  Foundation\\
Chuck Hansen, Los Alamos National Laboratory ACL\\
Anthony Hearn, RAND\\
Robert Harrison, Battelle-PNL\\
Michael Heath, University of Illinois\\
Kristin Hessenius, NASA Headquarters\\
Danny Hillis, Thinking Machines Corp.\\
Dan Hitchcock, Department of Energy\\
Jeff Holand, U.S. Army Engineer Waterways Experiment Station\\
Lee Holcomb, NASA Headquarters\\
Terry Holst, NASA Ames Research Center\\
Sally Howe, National Institutes of Health\\
Fred Howes, DOE\\
Paul Hunter, NASA Headquarters\\
Mack Hyman, Los Alamos National Laboratory\\
Steve Isakowitz, DOE Office of Management and Budget\\
Barry Jacobs, NASA Goddard Space Flight Center\\
Van Jacobson, Lawrence Berkeley Laboratory\\
Calvin Johnson, DCRT, NIH\\
Frederick Johnson, National Institute of Standards \& Technology\\
Gary Johnson, Department of Energy\\
Lennart Johnsson, Thinking Machine Corp.\\
Anita Jones, University of Virginia\\
Bob Kahn, CNRI\\
Jeffrey Kalb, MasPar Computer Corp.\\
Charles Kalina, National Institutes of Health\\
Malvin Kalos, Cornell University\\
Ken Kennedy, Rice University\\
Christopher Kerr, Princeton University\\
Bill Kirchhoff, DOE\\
Thomas Kitchens, DOE\\
Kenneth Kliewer, DOE\\
Chuck Koelbel, Rice University\\
Sylvain G. Korzennik, Smithsonian Astrophysical Observatory\\
Norman Kreisman, Department of Energy\\
George Lake, University of Washington\\
Monica Lam, Stanford University\\
Meemong Lee, Jet Propulsion Laboratory\\
Creon Levitt, NASA Ames Research Center\\
Kai Li, Princeton University\\
Donald Lindberg, National Institutes of Health\\
Jerry Linn, National Institute of Standards \& Technology\\
Rik Littlefield, Battelle-PNL\\
Fred Long, National Oceanic and Atmospheric Administration\\
John Lou, Jet Propulsion Laboratory\\
Steve Louis, Lawrence Livermore National Laboratory\\
Sandy MacDonald, National Oceanic and Atmospheric Administration\\
Jacob Maizel, National Institutes of Health\\
Allen Malony, University of Oregon\\
Vincent Marier, National Security Agency\\
George Marsaglia, Florida State University\\
Robert Martino, National Institutes of Health\\
Michael Mascagni, Supercomputing Research Center\\
Edward Masi, Intel Scientific Computers\\
Daniel Masys, National Institutes of Health\\
Edward McCracken, Silicon Graphics Computer Systems\\
Greg McRae, Lawrence Livermore National Laboratory\\
Scott McNealy, Sun Microsystems, Inc.\\
Carlos Roberto Mechoso, University of California, Los Angeles\\
Piyush Mehrotra, ICASE (USRA)\\
John Mellor-Crummey, Rice University\\
Paul Messina, California Institute of Technology\\
Barton Miller, University of Wisconsin\\
Ray Miller, National Security Agency\\
Clark Mobarry, NASA Goddard Space Flight Center\\
Reagan Moore, San Diego Supercomputer Center\\
Robert Morris, National Security Agency\\
Richard Robert Muntz, University of California, Los Angeles\\
Dave Nelson, Department of Energy\\
Mike Nelson, Office of Science and Technology Policy\\
Les Nichols, NASA Lewis Research Center\\
Mike Norman, University of Illinois NCSA\\
Joan Novak, Environmental Protection Agency\\
Ed Ott, University of Maryland\\
James Ousley, Control Data Systems, Inc.\\
Ross Overbeek, Argonne National Laboratory\\
Robert Palmer, Digital Equipment Corp.\\
V. Pankoni, National Science Foundation\\
Janice Patrick, IBM Federal Systems Co.\\
Merrel Patrick, National Science Foundation\\
David Patterson, University of California\\
Michael Pavloff, Silicon Graphics\\
Larry Peterson, University of Arizona\\
Eckhard Pfeiffer, Compaq Computer Corporation\\
Lawrence Picha, NASA\\
Lewis Platt, Hewlett-Packard Company\\
Gary Pope, University of Texas at Austin\\
Terrence Pratt, NASA Goddard Space Flight Center\\
Tom Pyke, National Oceanic and Atmospheric Administration\\
Irene Qualters, Cray Research, Inc.\\
Cal Ramos, National Institutes of Health\\
Daniel Reed, University of Illinois\\
David Robard, DCRT, NIH\\
John Rollwagen, U.S. Dept of Commerce\\
Richard Rood, NASA Goddard Space Flight Center\\
Joan R. Rosenblatt, National Institute of Standards \& Technology\\
Ralph Roskies, Pittsburgh Supercomputer Center\\
Robert Rosner, University of Chicago\\
Bruce Ross, Geophysical Fluid Dynamics Laboratory\\
Armistead G. Russel, Carnegie Mellon University\\
Manuel Salas, NASA Langley Research Center\\
Joel Saltz, University of Maryland\\
Ahmed Sameh, University of Illinois CSRD\\
William Scherlis, DARPA/SISTO\\
Klaus Schulten, Beckman Institute, University of Illinois at Urbana\\
L. Ridgway Scott, University of Houston at University Park\\
Mary Anne Scott, DOE\\
John Sculley, Apple Computer, Inc.\\
Kamy Sepehrnoori, University of Texas at Austin\\
James Sethian, University of California\\
Peter Shames, Jet Propulsion Laboratory\\
Bruce Shapiro, FCRDC\\
Margaret Simmons, Los Alamos National Laboratory\\
Horst Simon, NASA Ames Research Center\\
Ronald Skates, Data General Corp.\\
Burton Smith, Tera Computer Co.\\
Lauren Smith, Supercomputing Research Center\\
Paul H. Smith, NASA Headquarters\\
David Smitley, Supercomputing Research Center\\
Marc Snir, IBM T.J. Watson Research Center\\
Jerry Solomon, California Institute of Technology\\
Stephen Squires, Defense Advanced Research Projects Agency\\
Guy Steele, Thinking Machines Corp.\\
Thomas Sterling, NASA Goddard Space Flight Center\\
Kenneth Stevens, NASA Ames Research Center\\
Rick Stevens, Argonne National Laboratory\\
Walter Stevens, NIST\\
Malcom Stocks, Oak Ridge National Laboratory\\
James M. Stone, University of Maryland\\
Michael Strayer, DOE\\
Max Suarez, NASA Goddard Space Flight Center\\
Bob Sugar, University of California, Santa Barbara\\
Francis Sullivan, Supercomputing Research Center\\
D. Lansing Taylor, Carnegie Mellon University\\
Frank Tower, National Oceanic and Atmospheric Administration\\
J. G. Treybig, Tandem Computers\\
Lew Tucker, Thinking Machine Corp.\\
Sait Umar, Vanderbilt University\\
James Unruh, Unisys Corporation\\
Anthony Villasenor, NASA\\
Robert Voigt, National Science Foundation\\
Al Wagner, Argonne National Laboratory\\
David Walker, Oak Ridge National Laboratory\\
Steven Wallach, Convex\\
Michael Warren, Los Alamos National Laboratory\\
Harvey Wasserman, Los Alamos National Laboratory\\
Richard Watson, Lawrence Livermore National Laboratory\\
Howard Watts, Cray Computer\\
Gilbert Weigand, DARPA/CSTO\\
Jack Wells, Oak Ridge National Laboratory\\
Mary Wheeler, Rice University\\
Andy White, Los Alamos National Laboratory ACL\\
Elizabeth Williams, Supercomputing Research Center\\
Irving Wladawsky-Berger, IBM Corporation\\
Stephen Wolff, National Science Foundation\\
Paul Woodward, University of Minnesota AHPCRC\\
John Wooley, DOE\\
Patrick Worley, Oak Ridge National Laboratory\\
Chee Yap, New York University\\
Jeffrey Young, Environmental Protection Agency\\
Steven Zalesak, NASA Goddard Space Flight Center\\
Thomas Zang, NASA Langley Research Center\\
Mary Zosel, Lawrence Livermore National Laboratory\\
Wojciech H. Zurek, Los Alamos National Laboratory\\
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