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\begin{document}
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\begin{project}
\begin{titlepage}
	\projtitle{HPCC in Finance}
	\doctitle{
         Applications\\
              of \\
         NPAC\\
         High-Performance Computing and Communications Technologies\\
        in\\
         the Financial Industry\\
                 }
		\author{Miloje S. Makivic}
		\date{January 16, 1996}
		\version{0.4}
		\summary{Added Glossary}
	\id{HPCCinF}
	\docauthor{M~S~Makivic}
%	\doccontributor{KAH}
	\security{Public Domain}
\maketitle
	\approval{Geoffrey C. Fox}
\vfill
	\begin{history}
		\release{
			\author{Miloje S. Makivic}
			\date{December 1, 1995}
			\version{0.1}
			\summary{Initial draft}
			}
		\release{
			\author{Miloje S. Makivic}
			\date{December 15, 1995}
			\version{0.2}
			\summary{Delivered to Mr. Goldberg}
			}
                \release{
                        \author{Miloje S. Makivic}
                        \date{January 9, 1996}
                        \version{0.3}
                        \summary{Major revision (Quantitative Methods)}
                        }

	\end{history}
\end{titlepage}

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\begin{abstract}

This report contains an exposition of 
financial engineering and financial services applications
which could utilize High-Performance Computing and Communications Technologies
(HPCC) developed or deployed at Northeast Parallel Architectures Center (NPAC)
at Syracuse University. 
{\bf Keywords:} Simulation on
Demand, Monte Carlo simulations, hedging, time series analysis,
World Wide Web, futures, options, 
technical analysis, trading,
parallel computing, distributed computing, object-oriented technology,
data mining.

\end{abstract}

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\section{Introduction}
High-performance computing and communications, and, in  particular,
cooperative, distributed and parallel
computing, will play an increasingly important role in trading, financial
engineering, and all aspects of investment and commercial banking, 
due to a convergence of the following factors:
\begin{itemize}

\item Increased volatility due to globalization of financial markets
\item Global distribution of data sources
\item Increase in complexity of derivatives and other risk management vehicles
\item Increased demand for real-time investment and asset allocation 
      decision support
\item Increased volume of raw data and need to process large databases
\item Increased volume on the retail side of the spectrum mainly 
      due to on-line technologies (Internet and WWW)

\end{itemize}

High-performance computing technologies are indispensable in the application
domains such as:

\begin{itemize}

\item Quantitative Analysis:
   \begin{itemize}
        \item Derivative valuation, particularly over-the-counter products
        \item Portfolio optimization, valuation and asset allocation
        \item Hedging of large portfolios in real time
        \item Computational support for arbitrage trading
        \item Risk analysis simulations
   \end{itemize}

\item Data Mining
   \begin{itemize}
        \item Pattern recognition
        \item Detection of trends
        \item Detection of fraud
        \item Market segmentation
        \item Development of hedging or arbitrage strategies 
              based on historical patterns and analogies
   \end{itemize}

\end{itemize}
Traditionally, these applications supported the wholesale end of the
financial services spectrum.
An enormous new opportunity domain for HPCC is being created by the
emergence of Internet as the new economic paradigm.
The Internet is creating a shift among financial services providers
towards the retail end of the spectrum. At the same time, there is
increased demand on the retail side for more structured and more complex
financial instruments to manage risk with more flexibility.
Retail investors are becoming more self-reliant for investment decisions and
more comfortable with technology. This trend will be
reinforced by the wealth of information offered on the Internet.
In the short term, it will lead to increased demand for on-line
services in two areas:
\begin{itemize}
\item Investment education and training
\item Resources for investment decision support
\end{itemize}
In the long term, these trends, combined with globalization of markets
and the pervasiveness of the Internet will most probably lead to:
\begin{itemize}
\item Global continuous electronic trading
\item Elimination of distinction between exchange traded and OTC
      instruments 
\item Particle Finance as defined by C. Sanford of Bankers Trust (The Economist, March 26th, 1994).
\end{itemize}
These trends will inevitably lead to pervasive deployment of {\em scalable} 
high performance architectures to support market demands.

\section{An Overview of NPAC Technologies}

NPAC is at the forefront of development and deployment of advanced
information technologies relevant to the financial industry.
We focus on designing high-performance scalable architectures
by utilizing state-of-the-art computing and networking technologies.
On one hand, NPAC is developing 
advanced World Wide Web technologies that will
transform global, regional or organization-level webs of public and private
networks into a World Wide
Virtual Machine or Organization Wide Virtual Machine
for distributed computing, interactive distributed information
systems and complex communications. On the other hand, we engage in
research and implementation of
the state-of-the-art quantitative and analytical tools and methodologies,
with the ultimate goal of transparently integrating them using Web
technologies into a
seamless cooperative computing environment, capable of supporting all
aspects of management information processing, decision making process and
risk analysis. NPAC is committed to the the emerging
Web technologies such as Java and JavaScript because they will supply the integration
framework for geographically dispersed computing and
database resources which employ heterogeneous native and legacy technologies.

We build applications by implementing core analytical modules using the
most appropriate (regarding performance metric) software and hardware environment and
then integrate these modules into a network-friendly service
using Java and other Web technologies.

NPAC activities are backed by a unique
expertise in scientific computing, high-speed networking,
databases and parallel and
distributed processing. NPAC's expertise spans a wide variety of
computing platforms
(DEC, Intel, Sun, Thinking Machines, SGI, IBM, Cray, MasPar and others)
from desktops to high-end workstations to massively parallel supercomputers,
heterogeneous client-server environments, Windows and UNIX environments,
parallel database servers (Oracle, DB2) etc.
Our unique proficiency in heterogeneous computing environments and
system integration is quite valuable as the increasing number of
enterprises migrates towards client-server technology and network-centric
computing.

NPAC is an advisory member of the Financial Services
Technology Consortium (FSTC), which is a consortium of financial services
industry leaders, research institutions and technology providers whose goal
is to enhance the competitiveness of the US financial services industry.
FSTC emphasis is on projects involving the development of the
National Information Infrastructure (NII) and High Performance Computing
and Communications Initiative (HPCCI). We are working with the FSTC on
development of Web based infrastructure for inter-bank sharing of
fraud-related information and development of a distributed cooperative
environment for fraud-related data mining operations.

The list below shows some of the NPAC activities and areas of research
where either final products or
methodologies are directly relevant for financial industry applications.
For a complete description of NPAC Projects and Technologies, the reader
is advised to consult our Web pages at
{\bf http://www.npac.syr.edu/}. 

\begin{itemize}
\item Parallel Databases: Oracle and DB2 parallel RDBMS are being benchmarked
      and integrated into Web applications to support extremely large databases,
      high transaction frequency and highly complex transaction processing.
      This technology would be indispensable for IARC implementation.
\item Web/Oracle Integration: NPAC has created and put into operation several
      database applications that build Web pages from an Oracle 7 database
      server. These applications use the prototype interface software
      endorsed by Oracle, known as Web-Oracle-Web (WOW). Test applications
      include newsgroup archives, electronic mail archives of NPAC researchers,
      Oracle TextServer etc. The integration of the backend RDBMS server enables
      complex queries, sophisticated calculations and dynamic content, which
      will be the norm for future information providers.
\item The Living Schoolbook Project: combines Web technologies, digital image
      databases, multimedia on demand, parallel processing of GIS data, high
      performance multimedia servers and high-speed ATM networking to
      prototype the Education Information Infrastructure of the future
      in NY state K-12 schools. The experience gained in this project would
      be very valuable for the Investor (Trader) Education Center development.
\item VASTNET (Video Archive Server Technology): creates a prototype video
      "wire service" and archive system for CNN with Syracuse University
      Newhouse School of Communications using a Web interface customized
      for news professionals to query a digital video archive over a high
      performance ATM network. This technology could be adapted to deliver
      news feed to a trader's desk with customizable built-in filters and search
      capabilities.
\item Video on Demand effort at NPAC is defining and developing an architecture
      which integrates various system components such as storage, server, network,
      user interface and data capture. Among other things, NPAC implemented a
      client-server architecture which incorporates streaming, real time video
      delivery integrated with Web front ends. Such an architecture would be
      very valuable in trading rooms with heterogeneous desktop
      platforms. 
\item Physics  105 - Science for the 21st Century and
      Computational Science course materials offered at NPAC have course modules
      being constructed entirely as Web pages. A tutorial on technology and
      approaches for using HPCC and Web technology for distance learning and
      metacomputing can be found at
      {\it http://www.npac.syr.edu/users/gcf/hpdctutorial.html}.
\item Financial Engineering and Risk Management: NPAC has a strong effort in developing
      advanced valuation and simulation methods for exotic derivatives, such
      as Monte Carlo methods, binomial trees, non-parametric valuation,
      maximum entropy methods and solving Black-Scholes-type equations
      on massively parallel machines. The importance of this
      technology will continue to grow with proliferation of new types of
      increasingly complex derivative products and the demand for real-time
      position monitoring and marking-to-market. 
      NPAC is developing a proprietary
      risk analysis module based on Monte Carlo simulations, Scenario
      Methodology and large-scale correlated time series modeling  and
      estimation for a Wall
      Street InfoMall
      partner. Our efforts in this area build upon the world-class expertise of
      NPAC's research staff in scientific computational methods, physical
      sciences and statistical data analysis.
\item Simulation on Demand Technologies : By linking powerful compute servers,
      state-of-the-art visualization environments and high-speed
      networks, NPAC prototyped the future distributed environment for learning,
      decision support and even video games. Experience gained in this process
      is directly relevant for all information services providers on the Web.
\item Data Mining: NPAC is involved in the FSTC Data Mining and Fraud Detection
      Project. The goal of the project is to deploy a Web-based infrastructure
      for sharing the information about fraud and fraudulent activities
      among member banks (FraudWire) as well as to develop a data mining
      system for fraud detection in transaction databases. 
      The analytical methods
      include
      neural networks, radial basis functions, 
      global optimization algorithms and statistical techniques.
\end{itemize}

In the remainder of this document we will describe
in more detail our efforts in developing quantitative methods for
financial engineering, hedging and risk analysis.
Finally, we will illustrate how
these back-end quantitative methodologies can be integrated using
Web technologies into
value-added services on global and private corporate networks.


\section{Derivatives}

We have developed a novel path integral Monte Carlo method for pricing of derivative
securities \cite{art:mm:1995}. Standard Monte Carlo methods are founded
on direct simulation of the underlying stochastic process. Path integral
Monte Carlo approach relies on the probability distribution of the
complete histories of the underlying security, from present time to
the contract expiration date. In our present implementation,
Metropolis algorithm is used to sample
probability distribution of histories (paths) of the underlying security. The
advantage of path integral approach is that complete information
about the derivative security, including its parameter sensitivities
is obtained in a single simulation. It is also possible to obtain results
for multiple values of parameters in a single simulation. Therefore,
path integral approach provides a complete solution for the valuation problem
from a practitioner's viewpoint.
Monte Carlo should be the method of choice for option
valuation problems in parallel computing environment due to excellent
scalability. Our algorithm can price European as well as American contracts,
path dependent options, exotic options, arbitrary stochastic processes, including empirical
probability distributions, and it is capable of pricing contracts with
arbitrarily high-dimensional
underlying. The description of the algorithm can be found on-line at
{\bf http://www.npac.syr.edu/users/miloje/Finance/Derivatives/PIMC/option.html}.

We have also developed parallel algorithms for binomial option pricing models
for European and American contracts, including models with stochastic
volatility, on heterogeneous computer architectures, including
mixtures of MIMD and SIMD parallel machines, as well as data parallel and message
passing software. We used AVS data flow networks as the system integration
environment. Further information can be obtained in references \cite{Mills-Vinson-Cheng:1992} and
\cite{Cheng-Mills-Fox:1993}.

A doctoral student is developing parallel algorithms for solving Black-Scholes
type equations for multiple parameter sets and boundary conditions. She is 
developing a new approach that would take advantage of inherent parallelism
in both temporal domain and along the underlying price axis. The algorithm
exploits artificial relaxation dynamics to reach the solution for one parameter
set starting from a "nearby" parameter set. 

In addition to valuation of derivatives, NPAC is developing algorithms to infer
underlying probability distributions from observed market prices of options and
historical data for the underlying.
In its simplest form this amounts to the computation of implied volatility.
It is known that implied volatility matrix may contain significant variations
across strike prices and contract maturities. This implies that parametrization
of the underlying process via single volatility parameter is inconsistent
with market data. Instead, we formulate an approach which allows one to
generate a fully consistent estimate of the complete propagator for the underlying.
Using the constraints derived from the derivative instrument market prices (posterior information)
and the assumed underlying propagator (prior information), we employ the
Maximum Entropy framework to obtain an estimate of the underlying
propagator which incorporates statistically relevant information from both
prior knowledge and market data. Maximum Entropy formalism generates
a probability distribution which minimizes the information-theoretic
divergence from the prior probability subject to constraints given by
the observed market prices. In addition to treating the observed prices
as hard constraints, we use the bid-ask spread to introduce soft
constraints from market data, which can be treated in a Bayesian fashion
along with the MaxEnt principle.  
We believe that this methodology would be a very valuable tool for an arbitrageur.
To achieve its timely execution, it is necessary to employ massively parallel
machines.
This work will be presented at the Conference on Computational Intelligence for
Financial Engineering (CIFEr), March 24-26, NYC.

In conjunction with these high-end quantitative methods, we are in the process of
developing Java and
possibly virtual reality front ends to deliver the many dimensional information
sets generated by the simulations to the end-user's desktop. We are also using Java
and JavaScript to prototype an AVS-like data flow system with transparent access to
distributed resources such as historical databases and compute servers.
One of the design goals is to enable the user to interactively design new derivative
instruments.

\section{Hedging and Risk Management}

NPAC is collaborating with a prominent Wall Street vendor of high-end
integrated risk management systems in developing simulation and analysis
engines. We have developed Monte Carlo simulation software capable of
representing arbitrary stochastic processes and time series models and
designed to handle thousands of correlated risk factors. We have
developed calibration modules which analyze historical market rates and
use large scale global optimization algorithms to perform maximum likelihood
estimation of rate processes. We have developed for this application
two robust global optimizers capable of handling thousands of degrees
of freedom and objective functions with complex topology.
Both methods rely on stochastic algorithms which are similar to
simulated annealing but have features which are superior to the
annealing algorithm. The system is extensible and allows users to
add and experiment with their own libraries of stochastic processes.
Both the Monte Carlo module and the Calibration module are augmented with
a data analysis library. The software was recently successfully tested on
J.P. Morgan RiskMetrics datasets.

We have just begun extending the use of our Monte Carlo and optimization engines to
problems beyond short term risk analysis such as Value at Risk problems.
We are developing a stochastic, multiperiod portfolio optimization software
on parallel and distributed platforms. A doctoral student has identified
a number of portfolio optimization problems described by S. Zenios in his
book on Financial Optimization which require a parallel supercomputer
for timely execution on large portfolios. Among them are option portfolios
with realistic treatment of volatilities and interest rates across maturities
and instruments. This is a highly non-linear model with complex interactions
among the underlying degrees of freedom. Another class of models she will be
focusing on are the multiperiod, stochastic planning models. The final result
of this research will be a portable portfolio optimization library written
in "C" with the Message Passing Interface (MPI) library which guarantees
portability and performance.

Within the context of portfolio optimization we are also addressing the issue
of hedging, which is treated as the problem of minimizing the variance of a
portfolio which contains assets and hedge vehicles in a multiperiod
context using historical data to estimate stochastic processes of risk
factors or user-supplied scenarios. We intend to integrate realistic
effects of transaction costs for various hedging strategies
into the optimization procedure. This requires that hedging strategies
be treated as whole paths in a multiperiod scheme. The problem is
extremely computationally intensive since it requires that prices of
derivatives used as hedge vehicles be computed for every scenario.
The problem however has many sources of concurrency which can be very
efficiently exploited even on general purpose workstation networks.

On the other hand, situations may arise when a large number of simple
hedges must be constructed using standard techniques with derivatives,
such as delta, gamma, rho, vega and other hedging strategies. In this
context, it is essential to be able to compute option parameter sensitivities
as quickly as possible. The Path Integral Monte Carlo method was
designed to be able to compute all the greeks in a single simulation.
Its ability to span a finite window of parameters in a single simulation
further enhances  its applicability for hedging purposes.


\section{Investor Education Center}

The last two sections will describe prototypes of new services that
are enabled by integration of high-end compute and database servers
into the World Wide Web. It should be pointed out that NPAC has
extensive research, design and development experience in each of the
key technology components required by these services.

The World Wide Web is an ideal platform for offering sophisticated 
interactive environment for education or training.
Many institutions on the Web offer some of their training and educational
material on-line.
This, typically, static material can be used as a foundation
to develop a full-service Web-based Investor
Education Center. 

Let us focus for the moment on services that can be offered to the investment
community.
In addition to text, charts and images (static content),
the Web-based courses could include simulations of the complete
investment process with varying degrees of interactivity.
Relevant Web-related technologies are HTML forms, CGI, Perl, Java and VRML.
Level of interactivity can be tailored to the user's need and experience.

At the lowest level, users can browse predefined course material  
enhanced by visualization software to display quantitative data, as well
as images,
sound and video clips. At this level, content and presentation are
determined by the course designer. 

At the highest level of interactivity, a technical futures trader, for example,
might be guided through the  development of a trading system on-line using
the analytical tools explained in the introductory portion of the course, 
such as neural networks, and 
would be given a chance to test the system using
historical and live data from an exchange, information
sources such as news video clips, and external data such as global
economic indicators. 
The virtual trading experience could 
include realistic effects of slippage, commission schedules, etc. 

The Investor Education Center can be developed without a major investment in
hardware, although a separate database server would be necessary to
deliver training data sets ( market data,  multimedia content, etc.) and
a separate compute server should host analytical software modules used in 
the coursework. For example,
a trader might be taught how to use neural networks for time series
forecasting. Historical data should reside on the database server,
while the neural network software should
run on the compute server. Activities of these servers would be
coordinated by the Center's Web server, which would formulate
queries and computational tasks based on user inputs, forward them to
the two back-end servers, coordinate the exchanges between them,
collect computation results, and deliver final HTML pages to the user's
browser.

Major effort will be required for software integration and/or development of
analytical software demos, if the existing ones cannot be easily integrated. 
Major effort is also required to restructure the course content to
take advantage of the interactivity of the Web. 

Interactive content is essential for a successful Web presence. Besides drawing
a large number of prospective clients, it also offers a valuable source
of information about market needs and sentiments. By carefully tailoring
the content and monitoring access statistics, an organization can obtain
insights into the needs of its client base which are impossible to obtain
at such a low cost by any other method. 
For example, a very ambitious
service that may be offered by a financial service provider on the Web is the 
Investment Analysis Resource Center, described below.
Unlike The Investor Education Center, 
The Investment Analysis Resource Center would
consume significant infrastructure and computing resources with the goal of
offering sophisticated
analysis and decision support for the investment public. The problem is
that the Web is so new that it is not possible to predict what kind of
service the
Internet community would be willing to pay for and what the appropriate fee
schedule would be. A possible marketing strategy would be to introduce demos of services
to be offered by the Investment Analysis Resource Center through
the Investor Education Center, and then gauge user response. In addition to
gathering valuable information, the service provider will create a pool of likely
future users/clients.

\section{Investment Analysis Resource Center}

The explosion of the Internet is partially due to the fact that information
can be inexpensively offered on the Web, as well as
conveniently retrieved. The accessibility 
encourages an enormous amount of {\em raw
data}. In future, successful information providers
on the Web will be distinguished
by bundling raw data with tools for 
data refining, data analysis and presentation
of complex multi-dimensional data sets. This model of information delivery
on the Web is derived from
the object oriented model of computation, where data and methods which
operate upon the data are bundled together. As a result, users do not
purchase software to solve their problems, they purchase services from
on-line providers using standard interfaces which are independent of
their hardware or operating system.

This can be implemented with
the existing Web technologies, although they are
not specifically developed for this purpose. They 
are being augmented with technologies like Sun's Java, 
Netscape's LiveScript and JavaScript, IBM's Bart and
LotusScript, Microsoft's Visual Basic take-off of Java, and
General Magic's Telescript to specifically support this new breed of 
Web service providers.

Given these developments, it seems natural that a leader in innovative
financial services would eventually offer
a one-stop full-service Investment Analysis Resource Center. 
One of the most valuable components of the service would be 
sophisticated quantitative
analysis capability. At present, the retail investor is forced to use
primitive techniques for quantitative analysis, because of
prohibitively expensive software and hardware. In addition to
cost, investors must learn how to install, use and maintain
proprietary software and frequently must purchase different software 
for different
investment tasks. 

The idea behind the Investment Analysis Resource Center
is to enable the most sophisticated analysis of
investment data to a retail customer at the single initial 
expense of purchasing
a Web browser. 
Through a carefully designed Web front end, an investor would
be able to specify the kind of information he/she needs, the type of
analysis requested and how the results of the analysis should be presented.
A combination of existing Web technologies such as HTML forms and Java  
or VRML-based interfaces could enable a very high-level description of
the analysis request. 
For example, using HTML forms alone, one could
request "List three option contracts with the greatest discrepancy between
historical volatility and the implied volatility matrix among all
Feb 96 through May 96 pork belly puts and sort in increasing 
implied volatility order."

A request is shipped from the investor's Web browser to the 
IARC Web server.
The Web front-end is linked to an array of powerful back-end servers.
Typically, the IARC server would request services of historical database servers 
to obtain
historical data, or market servers for current pricing information. These data
would be forwarded to a compute server for numerically intensive tasks
like estimation of stochastic processes, option valuation, time series
analysis, forecasting, or application of technical analysis methods. The
result of this Simulation-on-Demand engine would be forwarded to a
visualization server, which would format the results according to
the investor's specification, prepare the results to be displayed on
the final Web page and ship them to
the IARC server, which would format and forward the Web page 
to the client's Web browser.  
The overall scheme is similar to the one described for the Investor
Education Center, but performance requirements of this system are
orders of magnitude higher.

Back-end servers must be able to process a large number of concurrent
users with computational tasks of varying degree of complexity. The
keywords here are performance,  scalability and fault tolerance, 
implying that both the Simulation-on-Demand engine and the
database servers would most likely be
parallel processors. 
From the business perspective, the size of the
initial investment in a system of this
functionality will be a barrier for all but the largest competitors.

The collection of database servers must offer a data warehouse-like
functionality to the Web front-end. The important performance issue
will be processing of complex ad hoc queries instead of high transaction 
volume, so the parallel query mechanism is important.
Task parallelism will be the dominant computational
model on the Simulation-on-Demand engine, which implies that both 
tightly coupled processors and distributed platforms may be appropriate.
Sophisticated resource allocation and scheduling mechanisms must be
put in place, since both the number of users and the complexity of
tasks are likely to be very volatile.

The most important aspect of this system is that it would radically change
the way investment decisions are made in the retail segment.
Retail investors would finally be able to purchase the most sophisticated
analysis available on the market without an upfront purchase of
an array of parallel compute
servers with installed million dollar simulation modules. 
Investors would have to pay-as-they-go only for resources they consume.
If they decide to try trading in another instrument, they do not
need new software. Convenience, cost structure and intrinsic value
would turn this site into one of the busiest on the Web within days.
A rudimentary prototype of this kind of service 
is offered on the Web by Olsen and
Associates ({\it http://www.olsen.ch/}). 
They offer forex trading recommendations generated by their
proprietary technical trading system based on intrinsic time volatility analysis.

The emergence of Java, a language environment from Sun Microsystems, Inc.
and the capability of delivering executable content in a secure fashion over
networks
is radically transforming the way information is propagated on the World Wide
Web. Java would be one of the most important technologies in the development of
the IARC. NPAC is at the frontier of deployment of Web computing technologies
based on Java and its derivatives. We are developing Web-based
GroupWare and other Web-top applications 
({\it http://www.npac.syr.edu/factoring/overview/WebTools.html } 
using standard language environments
such as Java, Perl, VRML and HTML, as well as NPAC's 
own WebScript and MOVIEscript.
Using Java, we have prototyped WebFlow, 
a Web metacomputer analog of data-flow 
environments such as AVS. 

To demonstrate capabilities of a World Wide Web Computer,
NPAC is attacking unsolved RSA challenge numbers, 130-digit integers for which there
exist no known factorizations, using massively parallel variation of either
the quadratic sieve or number field sieve factoring methods. The twist is that
our massively parallel resource is a collection of computers on the Web, from
SLIP connected PCs to biggest supercomputers. Web's client/server model and
its standard HTTP protocol is used
to automatize all aspects of computation, including organizational ones (like
registering new volunteers who wish to contribute their resources to this
effort) and resource allocation. More information about this project can be found 
at {\it http://www.npac.syr.edu/factoring.html}. The important thing to keep in mind
is that this technology applies to the World Wide Web as well as an Organization Wide
Web.

Finally, one should emphasize that Web-integrated parallel and distributed resources
are highly scalable. From the perspective of a trading or investment service provider,
this implies that service response can be tailored to the needs and resources of the
users, with the adequate fee schedule. With the commercially available job scheduling
and allocation tools, it is possible to offer anything from real-time to batch-like
processing of user requests on the same pool of processors. As the demands of the
users and their number grows, so can the size of the processor pool without any
changes in the underlying software.

\section{Glossary}\cite{{art:gcf:1995}}
\begin{itemize}
\item {\bf AVS} Visualization and integration software environment which allows users
      to construct their own visualization applications, by combining software components
      (modules) into executable flow networks. Modules may be executed on different
      nodes on a computer network.

\item { \bf Asynchronous Transfer Mode (ATM)} ATM is expected to be the
primary networking technology for the {\em NII\/} to support
multimedia communications.  ATM has fixed length 53 byte messages
(cells) and can run over any media with the cells asynchronously
transmitted.  Typically, ATM is associated with Synchronous
Optical Network (SONET) optical fiber digital networks running at
rates of OC-1 (51.84 megabits/ sec), OC-3 (155.52 megabits/sec) to
OC-48 (2,488.32 megabits/sec).

\item {\bf Black-Scholes Equation} One of the fundamental result in option pricing theory.
      A partial differential equation satisfied by the price of any derivative security
      dependent on a non-dividend paying stock.

\item {\bf CGI} Common Gateway Interface, a WWW standard for external programs to interface
      with Hyper-Text Transfer Protocol (HTTP) servers.

\item { \bf Clustered Computing} A commonly found computing environment
consists of many workstations connected together by a local area
network.  The workstations, which have become increasingly powerful
over the years, can together, be viewed as a significant computing
resource.  This resource is commonly known as cluster of workstations, and
can be generalized to a heterogeneous collection of machines with arbitrary
architecture.

\item { \bf COW or NOW} Clusters of Workstations (COW) are a particular
HPDC environment where often one will use optimized network links and
interfaces to achieve high performance.  A COW---if homogeneous---is
particularly close to a classic homogeneous MPP built with the same
CPU chipsets as workstations.  Proponents of COW's will claim that use
of commodity workstation nodes allow them to track technology better
than MPP's.  MPP proponents note that their optimized designs deliver
higher performance, which outweighs the increased cost of low-volume
designs, and effective performance loss due to later (maybe only
months) adoption of a given technology by the MPP compared to
commodity markets.

\item { \bf Data Mining} This describes the search and extraction of unexpected
information from large databases.  In a database of credit card
transactions, conventional database search will generate monthly statements
for each customer.  Data mining will discover using ingenious algorithms, a
linked set of records corresponding to fraudulent activity.

\item { \bf Distributed Computing} The use of networked heterogeneous
computers to solve a single problem. The nodes (individual computers)
are typically loosely coupled.


\item {\bf HTTP} The protocol spoken between Web clients and servers in order to send and
      receive hypermedia documents.  
\item {\bf HTML} Hyper-Text Markup Language is the standard language on the World Wide Web
      for creating and recognizing hypermedia documents. HTML includes markup elements for
      document formatting and extensions to support delivery of multimedia content over
      networks.
\item { \bf High-Performance Computing and Communications (HPCC)} Refers
generically to the federal initiatives, and associated projects and
technologies that encompass {\em parallel computing}, {\em HPDC}, and
the {\em NII}.

\item { \bf High-Performance Distributed Computing (HPDC)} The use of
distributed networked computers to achieve high performance on a
single problem, i.e., the computers are coordinated and
synchronized to achieve a common goal.

\item { \bf InfoVISiON} Information, Video, Imagery, and Simulation ON
demand is scenario where {\em
multimedia servers\/} deliver multimedia information to clients on
demand---at the click of the user's mouse.

\item { \bf Internet} A complex set of interlinked national and global
networks using the IP messaging protocol, and transferring data,
electronic mail, and {\em World Wide Web}.  In 1995, some 20 million
people could access Internet---typically by {\em POTS}.  The Internet
has some high-speed links, but the majority of transmissions achieve
(1995) bandwidths of at best 100~kilobytes/sec.  the Internet could be
used as the network to support a {\em metacomputer}, but the limited
{\em bandwidth\/} indicates that {\em HPDC\/} could only be achieved
for {\em embarrassingly parallel\/} problems.

\item {\bf Java} An object-oriented, distributed, interpreted, 
      architecture neutral,  multi-threaded programming language and environment
      produced by Sun Microsystems, Inc.

\item {\bf JavaScript} A fully interpreted client side scripting language
      used to build interacting multiframe user front ends.

\item { \bf Massively Parallel Processing (MPP)} The strict definition of
MPP is a machine with many interconnected processors, where `many' is
dependent on the state of the art.  Currently, the majority of
high-end machines have fewer than 256 processors.  A more practical
definition of an MPP is a machine whose architecture is capable of
having many processors---that is, it is scalable.  In particular,
machines with a distributed memory design (in comparison with shared
memory designs) are usually synonymous with MPPs since they are not
limited to a certain number of processors.  In this sense, ``many'' is
a number larger than the current largest number of processors in a
shared-memory machine.


\item { \bf Message Passing} A style of inter-process communication in
which processes send discrete messages to one another.  Some computer
architectures are called message passing architectures because they
support this model in hardware, although message passing has often
been used to construct operating systems and network software for
sequential processors, shared memory, and distributed computers.

\item { \bf Message Passing Interface (MPI)} The parallel programming
community recently organized an effort to standardize the
communication subroutine libraries used for programming on massively
parallel computers such as Intel's Paragon, Cray's T3D, as well as
networks of workstations.  MPI not only unifies within a common
framework programs written in a variety of exiting (and currently
incompatible) parallel languages but allows for future portability of
programs between machines.

\item { \bf Metacomputer} This term describes a collection of heterogeneous
computers networked by a high-speed wide area network.  Such an
environment would recognize the strengths of each machine in the
Metacomputer, and use it accordingly to efficiently solve so-called
{\em Metaproblems}.  The {\em World Wide Web\/} has the potential to be
a physical realization of a Metacomputer.

\item { \bf Metaproblem} This term describes a class of problems which is
outside the scope of a single computer architectures, but is instead
best run on a Metacomputer with many disparate designs.  These problems
consist of many constituent subproblems.  An example is the design and
manufacture of a modern aircraft, which presents problems in geometry
grid generation, fluid flow, acoustics, structural analysis,
operational research, visualization, and database management.  The
Metacomputer for such a Metaproblem would be networked workstations,
array processors, vector supercomputers, massively parallel processors,
and visualization engines.

\item { \bf Multimedia Server or Client} Multimedia refers to information
(digital data) with different modalities, including text, images,
video, and computer generated simulation.  Servers dispense this data,
and clients receive it.  Some form of browsing, or searching,
establishes which data is to be transferred.  See also {\em InfoVISiON}.

\item {\bf Multipurpose Internet Mail Extension (MIME)} The format used in
sending multimedia messages between {\em Web Clients\/} and {\em Servers\/}
that is borrowed from that defined for electronic mail.


\item { \bf Multiple-Instruction/Multiple-Data (MIMD)} A parallel computer
architecture where the nodes have separate instruction streams that
can address separate memory locations on each clock cycle.  All {\em
HPDC\/} systems of interest are {\em MIMD\/} when viewed as a {\em
metacomputer}, although the nodes of this metacomputer could have
{\em SIMD\/} architectures.

\item { \bf National Information Infrastructure (NII)} The collection of
{\em ATM}, cable, {\em ISDN}, {\em POTS}, satellite, and wireless
networks connecting the collection of $10^8$--$10^9$ computers that
will be deployed across the U.S.A. as set-top boxes, PCs, workstations,
and MPPs in the future.
The NII can be viewed as just the network infrastructure or the full
collection of networks, computers, and overlayed software services.
The {\em Internet\/} and {\em World Wide Web\/} are a prototype of the
NII.

\item {\bf Parallel algorithm} An algorithm which uses multiple cooperating processes to solve
      a computational problem
\item {\bf Parallel computer} A computer system made up of many identifiable processing units working
      together in parallel. The term is often used synonymously with concurrent computer to include both
      multiprocessor and multicomputer. The term concurrent generally dominates in usage in the USA,
      whereas the term parallel is the more widely used in Europe. 
\item {\bf Perl} Practical Extraction and Report Language, a scripting language available for UNIX and
      other operating systems, designed to enable easy manipulation of text, files and processes.
\item {\bf RDBMS} Relational Database Management System; software to manage a database in which data
      are stored by attribute.
\item {\bf Scalable} Capable of being increased in size, or more accurately, capable of delivering an
      increase in performance proportional to an increase in size. A scalable architecture is one that can be
      used as a design for arbitrarily large machines, or one whose increase in performance is linear in the
      amount of hardware invested. The term is also applied to programming systems, although its
      meaning is less clear in these cases. It is generally used to imply that the methods and algorithms
      employed in such systems are in principle capable of performing correctly equally well on large and
      small hardware systems. 

\item { \bf Single-Instruction/Multiple-Data (SIMD)} A {\em parallel computer\/}
architecture in which every node runs in lockstep accessing a single
global instruction stream, but with different memory locations
addressed by each node.  Such synchronous operation is very unnatural
for the nodes of a HPDC system.

\item {\bf Stochastic process} An infinite sequence of random variables, one for each value of time coordinate.
      Statistical properties of a stochastic process are generally defined by an infinite sequence of probability 
      functions for process variables at multiple points in time. Important simplifications apply for stationary
      Markov processes.

\item { \bf Virtual Reality Modeling Language (VRML)} A ``three-dimensional''
HTML that can be used to give a universal description of three-dimensional
objects that supports {\em hyperlinks\/} to additional information.

\item { \bf Web Clients and Servers} A distributed set of clients (requesters
and receivers of services) and servers (receiving and satisfying requests
from clients) using {\em Web Technologies}.

\item { \bf WebWindows} The operating environment created on the World Wide
Web to manage a distributed set of networked computers.  WebWindows is
built from {\em Web clients\/} and {\em Web servers}.

\item { \bf WebWork} An environment proposed by Boston University,
Cooperating Systems Corporation, and Syracuse University, which integrates
computing and information services to support a rich distributed
programming environment.

\item { \bf World Wide Web and Web Technologies} A very important software
model for accessing information on the Internet based on hyperlinks
supported by {\em Web technologies}, such as {\em HTTP}, {\em HTML},
{\em MIME}, {\em Java}, {\em Applets}, and {\em VRML}.


\end{itemize}

\bibliography{biblio}\bibliographystyle{alpha}

\end{project}
\end{document}


