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Part I]
The Science, Engineering, and
Technologies
In Part IT we take up the scientific, engineering, and technolog-
ical thrusts that were identified and summarized in Part ~ as most
likely to influence the evolution of the field in the next decade and
beyond. Here we provide more detail about their potential uses as
weld as some of the associated problems and prospects. Again, we
remand the reader to keep in mind two important points: first, there
are at least four technologies relevant to the link between computers
and productivity—computer technology, communication technology,
semiconductor technology, and packaging and manufacturing tech-
nology. We have focused on the first. Second, we are not presenting
here an exhaustive taxonomy of all the computer subfields and their
capabilities, but rather what we consider the most promising. The
absence of discussion on areas such as databases does not mean that
they are less unportant, but rather that they are likely to evolve fur-
ther prunarily through exploitation of the principal thrusts that we
do discuss, such as those in multiprocessors and intelligent systems.
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6
Machines, Systems, and Software
One of the biggest tasks facing computer designers is the devel-
opment of systems that exploit the capabilities of many computers
in the form of what are called multiprocessor and distributed sys-
tems. Related to current and future hardware systems is the task of
developing the associated software. These three topics are discussed
below.
MULTIPROCESSOR SYSTEMS
Multiprocessor systems strive to harness tens, hundreds, and
even thousands of computers to work together on a single task,
for example, to solve a large scientific problem or to understand
human speech and transcribe it to text. These systems involve many
processors located close to each other and with some means for
communicating with one another at relatively high speeds.
The effort to build multiprocessor systems is the consequence of
a powerful economic trend. Generational advances in VEST design
have made it possible to fabricate powerful microprocessors relatively
cheaply; today, for example, a single silicon chip capable of executing
1 million instructions per second costs less than $100 to make. But
the cost of manufacturing a very high-speed single processor, using a
number of chips and other high-speed components, has not declined
correspondingly. As a result, the world's fastest computers, which
41
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perform only hundreds of times faster than a single microprocessor,
cost hundreds or thousands of times as much. Consequently, link-
ing many inexpensive processors makes sound economuc sense and
raises the possibility of scalable computing, i.e., using only as many
processors as are needed to perform a given task.
Another import ant incentive to build multiprocessor systems is
related to physical limits. Advances in the speed of large, prunarily
single-processor machines have slowed down: after averaging a ten-
fold increase every 7 years for more than 30 years, progress is now
at the rate of a threefold increase every 10 years (Kuck 1986~. These
superprocessors are approaching limits dictated by conflicts between
the speed of light and thermal cooling: they must be small for infor-
mation to move rapidly among different circuits of the machine, yet
they must be large to permit dissipation of the heat generated by
the fast circuits. Multiprocessors are expected to push these limits
higher because they tackle problems through the concurrent opera-
tions of many processors. If the recent advances in superconductivity
result in effectively raising these limits for superprocessors, they will
also raise them for multiprocessors and thus the relative advantages
of multiprocessors over single processors will remain the same.
The key technological problems related to the creation of use-
fu] mutliprocessor systems include: (1) the Recovery and design of
architectures, i.e., ways of interconnecting the processors so that
the resulting aggregates compute desirable applications rapidly and
efficiently; (2) finding ways to program these large systems to per-
form their complex tasks; and (3) solving the problem of reliability,
i.e., minimizing failure of performance within a system in which the
probability of individual component failures may be high. Current
experunental work In the multiprocessor area includes exploration of
different processor-communication architectures, design of new lan-
guages, and extension of popular older languages for multiprocessor
programming. Theoretical work includes the exploration of the ulti-
mate limitations of multiprocessors and the design of new algorithms
suited to such systems.
The potential uses of multiprocessors are numerous and signifi-
cant. The massive qualitative increase in computing power expected
of multiprocessors promises to make these systems ideally suited to
large problems of numerical and scientific computing that are charac-
terized by inherent parallelism, e.g., weather forecasting, hydra- and
aerodynamics, weapons research, and high-energy physics. Perhaps
more surprisingly, conventional transaction-oriented computing tasks
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in banks, insurance companies, mrlines, and other large organizations
can also be broken down into independent subtasks—organized by
account or flight number, for example indicating that they can be
managed by a multiprocessor system as well as, and potentially more
cheaply than, by a conventional mainframe computer. In short, the
fact that current programs are sequential ~ not a consequence of
their natural structure in all cases, but rather of the fact that they
had to be written sequentially to fit the sequential constraint of
single-processor machines.
Most promising of ad, multiprocessors are viewed by many com-
puter scientists as a prerequisite to the achievement of artificial
intelligence applications involving the use of machines-for sensory
functions, such as vision and speech understanding, and cognitive
functions, such as learning, natural language understanding, and
reasoning. This view is based on the large computational require-
ments of these problems and on the recognition that multiprocessor
systems may imp ate in some primitive way human neurological or-
gan~zation: human vision relies on the coordinated action of millions
of retinal neurons, while higher-level human cognition makes use of
more than a trillion cells in the cerebrum.
Traditional supercomputers, which rely on one or a handful of
processors running at very high speeds, have already demonstrated
their utility in several important applications. The proven capabili-
ties of supercomputers will be greatly multiplied if the potential of
multiprocessors is realized, leading to the tantalizing possibility of
ultracomputers, which will harness together large numbers of super-
processors to yield mind-boggling computational power. Such power,
in turn, could be used to expand scientific capabilities, for example,
through computational observatories, computational microscopes,
computational biochemical reactors, or computational wind tunneb.
In these applications, massive-scale simulations would be performed
to addrem previously unsolved scientific problems and to chart un-
explored intellectual territory.
DISTRIBUTED SYSTEMS
Distributed systems are networks of geographically separate
computers collections of predominantly autonomous machines con-
trolled by individual users for the performance of individual tasks,
but also able to communicate the results of their computations with
one another through some common convention. E a multiprocessor
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system can be likened to several horses puDing a cart with a single
destination as their goal, then a distributed system can be likened to
a properly functioning society of individuals and organizations, pur-
suing their own work under their own planning and decision schemes,
yet also engaging in intercommunication toward achieving common
organizational or individual goals.
Multiprocessor systems link many computers primarily for rea-
sons of performance, whereas distributed systems are a consequence
of the fact that computers and the people who use them are scattered
geographically. Networking making it possible for these scattered
machines to communicate with one another—opens up the possibil-
ity of using resources more efficiently; more important, it connects
the users into a community, making it possible to share knowledge,
improve current business, and transact it in new ways, for exam-
ple, by purchase and sale of information and informational labor.
Networking also raises new concerns about job displacement; the po-
tential for the invasion of privacy; the dissemination and uncritical
acceptance of unreliable, undesired, and damaging information; and
the prospect of theft on a truly grand scale. These are reminders
that technology, like any innovation, carries with it risks as well as.
benefits and that safeguards must be provided to protect against
such incursions. Devising appropriate safeguards is itself an urgent
topic of theoretical systems research.
Distributed systems have emerged naturally in our decentralized
industrial society. Their emergence reflects the proliferation of com-
puters, especially the spread of personal desktop computers, and the
appetite of users for more and more information. It also reflects the
demands of the marketplace, in which users operate sometimes as in-
dividuals, at other times as members of an organization, and at stiD
Other times for interorganizational purposes. Distributed systems
rely on a range of communication technologies and approaches-
including telephone and local area networks, long-haul networks,
satellite networks, cellular, packet radio and optical fiber networks-
to connect computers and move information as necessary.
Distributed systems are at the basis of modern office automation.
They are evident in computer networks such as the ARPANET,
which has facilitated the exchange of ideas and information within the
nation's scientific community. Perhaps most significant, distributed
systems have begun to transform national and international economic
life, creating the beginnings of an information marketplace geared to
the exchange of information. Electronic mail enables communities of
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users to annotate, encapsulate, and broadcast messages with little or
no handling of paper and makes it possible to send people-to-people
or program-to-program messages. Customers from every part of the
country can tap into centralized resources, including bibliographic
databases, electronic encyclopedias, or any of a growing number of
specialized financial, legal, news gathering, and other information
services. Geographically separated individuals can pool resources for
joint work: a manual for a new product can be assembled with input
from the technical people on one coast and from the marketing staff
on the other; a proposal can be circulated widely for comments and
rebuttals from many contributors electronically. Homebound and
disabled individuals can participate more actively in the economy,
liberated by networking from the constraints of geographical isolation
or physical handicap. The technology of distributed systems, through
enhanced and nationwide network access, could have a major and
unique impact on the future economy.
The limitations of today's distributed systems, whether they
form a small local system in a building or a much larger corporate
communication system, inhibit the growth of an information mar-
ketplace. They do so because the systems communicate at a rather
low level, with the only commonly understood concepts being typed
characters and symbols. They also are often heterogeneous, made up
of machines from a variety of manufacturers, which employ a number
of different hardware and software conventions. Except at the lowest
level of communicating characters, there are no universally accepted
standard conventions (protocols), although such shared communica-
tion regimes represent the first level of software needed for providing
a higher level of commonality of concepts among such Separate
machines. As a result, if two computer program are currently to
understand each other in order, for example, to process an invoice,
they must be specifically programmed to do so. Such understanding
is not shared by other number of machines unless they are similarly
programmed. The greater the number of machines participating in
a Attributed system, the greater the agreement needed on com-
mon programming. Such agreement is not easy to arrive at, in part
because system heterogeneity makes it difficult to implement. Con-
sequently, one of the major problems ahead is the development of
common and effective distributed system semantics—that is, lan-
guages and intelligent software systems that will help machines com-
municate with and understand one another at levels higher than
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the communication of characters, and whose use is sunple and fast
enough to win acceptance among a large number of users.
Despite these obstacles, the number of interconnected systems is
increasing because of their great utility. The ongoing proliferation of
these computer networks provides a test bed for research and a pow-
erful incentive for developing an understanding of their underlying
principles. Beyond the need for Attributed system semantics, other
important aspects include the development of innovative and robust
system architectures that can survive computer and communication
failures without unacceptable losses of information, the development
of network management systems to support reliable network ser-
vice on an ever growing scale, and the creation and evaluation of
algorithms specifically tailored to Attributed systems. Finally, on
the software side of these systems, the problems associated with
programming large and complex computer systems must be better
understood.
SOFTWARE AND PROGRAMMING
Computers are general-purpose tools that can be specialized to
many different tasks. The collections of instructions that achieve
this specialization are called programs or, collectively, software. It is
software that allows a single computer to be used at various tunes (or
even sunultaneously by several users) for such diverse activities as
inventory and payroll computations, word processing, solving differ-
ential equations, and computer-ass~ted instruction. Programs are in
many ways similar to recipes, game rules, or mechanical assembly in-
structions. They must express rules of procedure that are sufficiently
precise and unambiguous to be carried out exactly by the machine
that is executing them, and they must allow for error conditions
resulting from bad or unusual combinations of data.
Unlike other products, the essence of software is in its design,
which is inherently an intellectual activity. This is so because produc-
ing many instances of a program involves straightforward duplication
rather than extensive fabrication and assembly. Accordingly, the cost
of producing software is dominated by the costs of designing it and
making certain that it defines a correct procedure for performing all
of its desired tasks—costs that are high because of the difficulties
inherent in software design.
Creating software involves devising representations for informa-
tion called data structures and procedures called algorithms to carry
out the desired information processing. One of the major difficulties
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associated with this task is that there are generally many different
combinations of data structures and algorithms that can perform a
desired task effectively. For example, in a machine vision system that
inspects circular parts, a circle could be represented by its center and
radius (a data structure of two numbers for every part) or by a few
thousand points that approxanate the circumference; the first data
structure is more economical but does not by itself permit deviations
from circularity to be represented, as does the second. In carrying
out the artful process of data structure and procedure selection, the
programmer must often pay equal attention to both large and small
software parts, like an architect who must design a house, down to
all its windows, doors, doorknobs, and even bricks.
Furthermore, it ~ difficult for programmers to anticipate during
design all the circumstances that might conceivably arise while a
program is being executed. Indeed, another difficulty involves the
illusion that software, because it is the stuff of design, is infinitely
malleable and can therefore be easily changed for improvement or
to meet new demands. Unfortunately, software designs are often
so complex and variations among different parts so subtle that the
implications of even a small change are hard to anticipate and control.
These factors make it fundamentally hard to specify and design
software. They also make software difficult to test by anticipation
of ah the failure circumstances that may accidentaBy arise during
actual operation. For these reasons, software development ~ often
quite costly and time consuming.
Beyond design, and after a program is put to use, modifications
are often required to repair errors, add new capabilities, or adapt it
to changes in other programs with which it interacts. This activity
is called software maintenance a misleading term since it involves
continued system design and development rather than the traditional
notion of fending oE the ravages of wear and age. Such maintenance
can amount to as much as 75 percent of life-cycle cost (Boehm 1981~.
In the 1960s the cost of computing was dominated by the cost
of hardware. As the use of computers became more sophisticated.
the cost of hardware dropped, and the salaries of programmers in-
creasecI, software costs came to dominate the cost of computing
(OECD 1985~. The problem has been aggravated by an appar-
ent shortage of good software professional and limited productivity
growth. The overall annual growth of programming productivity is
at best 5 percent (OECD 1985~. The increase in software costs is
taking place throughout the field, from small programs on personal
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computers to life-critical applications and supercomputing. The cost
increase is fueling efforts to improve software engineering through de-
velopment of tools to partially automate software development and
techniques for reusing software modules as parts of larger pieces of
software. A lot of work is needed to increase productivity ~ software
development by a factor of ten, considered a critical milestone in
industry.
Another serious software problem has to do with people's per-
ceptions and expectations. Hardware and software sound similar,
and people are frequently appalled that as the former gets cheaper
by some 30 percent per year, the latter stubbornly resists produc-
tivity improvements. Such a comparison, however, reflects a mis-
understanding of the nature of the software development process;
a brief recounting of the development of MAC SYMA, one of the
earliest knowledge-based programs, suggests why. To develop a re-
search prototype of that program a mathematical assistant capable
of symbolic integration, differentiation and solution of equations-
took 17 calendar years and some 100 person-years. In terms of the
number of moving parts and their relationship to one another, the
program's complexity was comparable to that of a jumbo jet, whose
design and development cost more than 100 tunes as much. Most
people can intuitively grasp the difficulties of constructing complex
physical systems such as jumbo jets. But the complexities and design
difficulties in the more abstract world of software are less obvious and
less appreciated.
Despite all tliese difficulties, software development has seen sig-
nificant progress. In the early days of programming, it was often
a triumph to write a program that successfully computed the de-
sired result. There was little widespread systematic understanding
of program organization or of ways to reason about programs. A1-
gorithms and data structures were originally created in an ad hoc
fashion, but regular use and research led to an increased fundamen-
tal understanding of these entities for certain problem domains: we
can now analyze and compare the performance of several proposed
algorithms and data structures and, for several kinds of problems, we
often know in advance theoretical limits on performance (see Chad
ter 8~. Sound theories have also contributed to the construction of
certain classes of software systems: for example, in the early 1960s
the construction of a compiler (a program that translates programs
written at a higher-level language to mach~ne-leve] programs) was a
significant achievement for a team of programmers; such systems are
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now constructed routinely (with largely automated means by a much
smaller group) for traditional single-processor machines.
In today's computing environment, escalating demands for over-
aD computer performance become escalating demands for software
capability and software size. Development of large systems requires
the coordination of many people, the maintenance and control of
many versions of the software, and the testing and remanufacture
of new versions after the system has been changed. The problems
associated with these activities became a focus of computer science
research in the m~-1970s, through techniques of modular decom-
position (Parnas 1972) and organization of large teams of program-
mers (Baker 1972~. At that tune, a Extinction was made between
progranuning-in-the-smaD and programming-in-the-large to cad at-
tention to the difference between the problems encountered by a few
people writing simple program and the problems encountered by
large groups of people constructing and managing sizable assemblies
of modules.
Beyond these more or less pure software systems that deal only
with information, there are other, even more complex, highly dis-
tributed systems that often interact with physical processes, such
as the U.S. telecommunications, air traffic control, transportation,
process control, energy, air defense, strategic offense, and command-
control-communication and intelligence systems. These supers
tems, as they have been called (Zracket 1981), grow over a period
of decades from initially limited objectives to evolutionarily mature
end states that are generally unpredictable at the start. The need
to create such supersystems and other software with sufficient reli-
ability for effective use presents a set of software design and devel-
opment problems that are not addressed by the techniques of either
programming-~n-the-smaD or programming-in-the-large.
Accordingly, two new tasks for software research are: (1) to de-
velop better techniques for designing software, especially software to
be embedded In very complex, real-tune application systems, and (2)
to use emerging artificial intelligence techniques for the development
of tools that wait help software developers manage the complexity of
such software. Other directions and opportunities for future software
progress include: (3) effective ways of improving the productivity of
the software development process, e.g., through automation of soft-
ware design, reuse of existing software components, or new types of
software architectures; (4) ways to reason about the correctness of
software, including the task specification process; (5) infrastructure]
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so
tools and resources, such as electronic software distribution systems;
and (6) addressing the new multiprocessor software problems that
will inevitably arise from the new multiprocessor architectures dis-
cussed above.
Representative terms from entire chapter:
data structures
