risc+reward_1988

8/7/2019 RISC+Reward_1988

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RISC and Reward

MetaTrends:

of RISC and Reward

In times of crisis and change, Knowledge is Power.

John Fitzgerald Kennedy 1961

Predicting is hard especially the future.

Niels Bohr (Copenhagen) 1938

On Foreknowledge

Wouldnt it be wonderful to know the future before it happens?

At the very least, it could be immensely profitable to know the next

number to come up on the roulette wheel or the winner of tomorrows

Daily Double. Such simple greed is more than the grist for the writers of

old Twilight Zoneor new Back to the Future IIscripts. After all, it was the

roll of the dice and the spin of the wheel that prompted Blaise Pascal to

develop the first mathematical theories of Probability and Statistics in the

early Seventeenth Century and the lure of the track and the table that

seduced the first computer programmer, the Countess Ada Lovelace, in

the early Nineteenth.

A century later, the lure of a peek at the future inspired the Dow

Theory, the quantitative analysis of the peaks, the valleys and the

opportunities of the securities trading floor, an application area far more

profitable and hence far more respectable (until recently) than the casino

floor. The raw data fueling this engine of risk and reward have been

published each trading day since the 1920s duly accompanied by itseponymous Industrial Average in the Dow-Jones Corporations ever-

respectable Wall Street Journal, theRacing Formof the more modern

breed of speculator.

As we near the end of the Twentieth Centurys literally exponential

growth of science, technology and the changes wrought by them, those


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most critically dependent on the ambiguous oracles of change are those

that stand to gain or lose the most on the roll of the technological die. It is

the investor, the entrepreneur and the user of these complex patterns of

information whether inside and outside the giant corporate and

government entities who most vitally depend on the new art and

science of managing complexity.

It is the thesis of this author and of his company from experience in

researching and forecasting the future for those entities and individuals

that the current evolutionary trend towards open systems is not an end in

itself. Indeed, we believe it is even more than a means for managing the

inevitable complexity of this complex information age. Instead, we see it

as the tip of an emerging iceberg, a manifestation of an inescapable

Darwinian force, the fabled Invisible Hand, reshaping our global

economy and society into a new connective topology or, in current

parlance, a new paradigm or returning to a very old, even prehistoric

paradigm. It is a practical consequence of our thesis that the shape of the

world under this fundamental new paradigm can be foreseen, anticipated

and acted upon.


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On Connections

If I have seen far it is because I have stood on the shoulders of

giants.

Isaac Newton (London) 1703

In other disciplines, people make progress by standing on each

others shoulders

in Computing, its seems, we make progress only by standing on each

others toes.

Edsgar Djikstra (ACM Pacific Conference, San Francisco) 1975

As the video-journalist-historian-philosopher James Burke has so ably

pointed out, the entire history (and, perhaps, prehistory) of the human race

is a history of connections. Human speech and its descendant forms of

communicationmake possible the cooperation that is the basis of our

societies, from the first protohuman tribes in paleolithic Africa to the

satellite-linked world economy of today. With the development of the

written word and number, communication in space was augmented by

communication in time from past to present to future and the growthof human culture and knowledge began its exponential growth, building

upon the knowledge of the past rather than reinventing it each generation.

For much of the last five-hundred years, from the Age of Exploration

onward, the physical aspects of connection transportation more than

communication historically has captured more of the attention of

society and had more visible impact on the economy. Just as in the

popular view, The Wheel is the most significant early invention, so the

roads of the Roman Empire, then the sailing ships of the various colonialEmpires, the steamship, the railroad, the automobile and the aircraft have

shaped our society both in deed and name. Weve had theSteam Age,

the Auto Age, the Jet Ageand the Space Age all within a single recent

lifetime. There seems to have been no comparable reference to the

Printing Age, the Telegraph Age, the Telephone Ageor any information-


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based equivalents until the currently referencedComputer Ageor

Information Age each implying something more tangible, less transitory,

less ephemeral than the abstraction of pure communication.

In analyzing this elusive societal phenomenon the aversion of theaverage human mind to recognize or acknowledge that which it cannot

grasp we have found as many explanations as there are experts, as

many opinions as there are observers. But from a purely practical and

directly utilitarian stance, at least one viewpoint recommends itself as a

successful technique in predicting the future, at least as it may be

extrapolated in a credible (if counterintuitive) way, from the past. The

trend, or perhaps a Metatrend(with apologies to John Naisbitt) which

emerges from this analysis is: The place to watch for fundamental

transformingchanges is at the interface between Goods and Services,

where new enablingtechnologies permit values once added by Services

now to be added by Products.


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On Forecasting

No more than 500,000 automobiles will ever be owned by families in

the United States,

as this is the greatest number of households which can afford the

requisite chauffeurs.

Unknown Market Predictor 1906

If telephone usage in these United States were to continue growing at

its current pace,

in fifty years the entire population would need to be employed as

telephone operators.

Anonymous Forecasting Wit 1928

Of course I am interested in the future I plan to spend the rest of

my life there.

Isaac Asimov (New York) 1963

Among the less pretentious and self-satisfied members of the marketresearch and technological forecasting communities (possibly an empty

set), the paraphrased predictions above stand as excellent examples of

the many dangers inherent in linear extrapolation. Modern forecasters

be they academic economists, government statisticians or private

prognosticators (i.e., financial analysts and employees of syndicated

market research firms) may smile at these naive efforts. But on semi-

log paper (or its Lotus 1-2-3 equivalent) our complex formulae for

CAAGRs (Compound Aggregate Annual Growth Rates) often plot to

equally boring straight lines, bland linear extrapolations transposed into

exponentials. Past events, on the other hand the histories of actual

shipments, installations and use, rarely look so clean or well-behaved

look very bumpy and irregular by contrast.


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This is not to say that this persistent error of the professionals is readily

avoidable or that Economics is called the dismal science because of its

dismal record of accuracy its predictions have compiled. The amateur

predictor, whatever his stripe, is on the whole even worse: the only truly

consistent Dow-Jones datum predicting the way the market will not go is

the odd-lots trading figure, the paw-print of the small investor. The

convoluted sine waves of the seesawing market where to be even

slightly out of phase, to be even slightly behind the information curve, and

hence perpetually wrong are only one form of the complex exponential

which the unaided human mind seems predisposed to linearize.

Mathematically, the same tendency to flatten the emotionally

incomprehensible and ever-steepening exponential growth curve compels

us to draw straight lines through some point in the near future. Thus wealways overestimate progress for the near future but grossly

underestimate the possibilities of the long run. The latter accounts for the

popular presss amusing technological predictions of decades past

personal commuter helicopters for intracity travel, ultra-streamlined 200-

mph cars for highways, 500-mph transcontinental trains and twelve-

propeller flying boats or atomic-powered airships for intercontinental trips.

Most significantly, both of the grossly underestimating

prognostications regarding the automobiles and the telephones futuremarkets were right in essence but wrong in interpretations based on their

metalinear mode of thinking from which we have no scientific escape

even today, only the artistic flight of the true visionary. In reality, both of

the predictions were correct in principle, but the essential value-added

component of each of these two industries were transformed (in their

essence) from reliance upon raw service functions to reliance on

products plus a support infrastructure, specifically:

Henry Fords economic advances in mass production and economies

of scale were paralleled by the technological advances of the electric

starter, synchromesh gears, pneumatic tire, automatic spark-

advance and later market-expanding enhancements like the

automatic transmission and power steering coupled with the

infrastructure of paved highways and refined petroleum followed by


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the automotive fuel and service network and interstate highway

system, converging to create an environment wherein we have all

become our own chauffeurs.

Bell Telephone Laboratories comparable advances in telephony,

beginning with the stepping relays that displaced the switchboard

and culminating in todays digitized, computer-routed, satellite-

relayed, Touch-Tone direct international dialing, have now

transformed the vast majority of the original service functions into a

global network of mass-produced products all interconnected by a

vast support structure in which we have all become our own

telephone operators.

The forgotten forecasters may indeed have the last laugh on their

latter-day critics, for thoughts and deeds based on linear or even

metalinearextrapolation are subject to the penalties set forth in The Origin

of Species. The American railroads once constituted a rich, inbred

oligopoly that looked no farther for its competition than the other side of

the tracks. In the passenger markets, they never looked upwards to the

unfriendly skies of primitive air travel; nor, in the freight transport markets,

did they recognize the burgeoning network of publicly funded highways

shared by long-haul trucks with an explosion of private autos. The Swiss

arrogance in their early dismissal of the externallydigital watch nearly costthem an entire industry when quartz technology moved in to replace the

timepiece's internals.

Lest we be too quick to judge, in our own backyard, the U S

semiconductor industry in the 70s virtually abandoned the slow high-

density, low-powered CMOS technology to the Japanese for

calculators, watches and other toy applications. Meanwhile, we pursued

the bipolar and ECL leading edge of computer speed and power for nearly

a decade until stopped by the twin bottlenecks of ultra-large, ultra-high-speed systems: the thermodynamic limits of speed-power product and

physical limit of the speed of light. Somewhat belatedly we noticed that

power consumption is ultimately only the flip side of power dissipation, and

device density is only the obverse of device-to-device path length. Closer

still to home, most of us remember how the giant mainframes of IBM were


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blind-sided by Digitals first minicomputers, which the King of Computing

once took no more seriously than, say, Digital took the first personal

computers two decades later. And so,ad infinitum.


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On Complexity

It is not the case that our problems are so complex, but that we have

such small heads.

Edsgar Djikstra in The Magic Number 7, plus or minus 2 1974

It isnt ignorance that gets us in trouble its what we know that aint

so.

Will Rogers (Tulsa, Oklahoma) 1932

To restate our primary thesis that the critical transitions in the

history of technology, the greatest impacts on industries, economies andsocieties, occur when service-based functions are transformed in to

product-based functions. In the Industrial Age context of the past few

centuries, mechanizationtransformed predominantly human-based tasks

into primarily machine-based ones more accurately machine-

augmented, since machine power was still under the control of human

intelligence. In the present information age context, whereautomation

seeks to provide machine augmentation of human intelligence, the

success of these attempts often depends upon the systems underlying

philosophy whether the design seeks toaugmentor to replacehuman

capabilities.

Virtually without exception, only the augmentation approach achieves

success: even the autoteller is less an automated replacement for a human

bank tellerthan an augmented and simplified system interface for the bank

customer. This redefines the basic design task (and the proble

encountered) from a computationfocus on information management, to a

communicationfocus on complexity management. Communication problems

whether between humans and programs, programs and systems, systems

and machines, or between machines in a network, programs in a system, or

humans in an organization are all problems in complexity. Consider five

arenas of competition in our industry, five points of concern, taken in sequence

from bottom to top, beginning at the hardwarelevel, namely:


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RISC: Reduced Instruction Set Computing Impact on computer

speed & standards

Open Software: Portability, scalability & interoperability of systems,

software & data

Open Systems: Impact & exploitation of networking,

multiprocessing & parallelism

GUIS: Graphic User Interface Systems Impact on human user

speed & standards

Systems Integration: All of the above + managing the complexity

they necessitate

Fundamentally, each of these five topics is either the solution to aspecific complexity management problem or an example of such a

problem or both. While this analysis could treat each of the five topics,

space and time permit us to examine in detail only the first the

problems, challenges and opportunities of RISC with an eye to tracing

the common thread connecting all this conferences topics, namely the

management of complexity.


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If complexity is the common problem then is software the common

solution or just another problem? From the viewpoint of a hardware

engineer or corporate management, software is at best a black art and at

worst a black hole, devouring countless millions of man-months and

woman-weeks each year with no guarantees of delivery on time, within

budget or even as specified. Endless analyses of the software

bottleneck all cite the need for better tools and greater discipline. Rarely

do they address the possibility that we may be tackling problems of

excessive complexity, those beyond our psychological limits upon the

number of items and interrelations we can mentally manipulate: the seven

(plus or minus) cited by computer-science savant Edsgar Djikstra. In

short, the first step towards our goal may be change in the way we thinkof

software (in specific) and complexity (in general.)

Were we seeking an alternative definition of the RISC buzzword at a

more general level, Reduced Intrinsic System Complexity would fit

perfectly with Gerald Weinbergs popular writings on general systems

theory, including An Introduction to General Systems Thinking, and Are

Your Lights On, but really beginning most appropriately with The

Psychology of Computer Programming, in 1969, when he was an IBM

fellow. One of his basic premises is that predictability occurs at either end

of the spectrum of complexity Newtons Laws of Mechanics holding

sway in simple domains with few elements, Boyles and Boltzmanns Laws

of Statistical Mechanics guaranteeing outcomes in innumerably complex

domains with Murphys Laws governing the intervening domain of

complex system behavior.

In examining the four critical technologies besides RISC (original

definition), we find that all five approaches attempt to circumvent Murphys

Laws a goal that may seem like thwarting the Second Law of

Thermodynamics. In reality each methodology attempts toreduce the

number of possible states the system can assume in Information

Theory terms to minimize its entropy to a complexity level our little

Size 72 heads can manage:


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In Open Software, the IEEE 1003 POSIX standard and its

progeny the N.I.S.T. Federal Information Processing Standard

used in United States government contracts, the X/Open Common

Application Environment embraced by European agencies and the

ISO Joint Technical Committee 1, Subcommittee 22 Working Group

proposal that hopes to grow up to be a Standard for all Seasons (and

reasons) are primarily Application Program Interface

specifications. Each seeks to spell out clearly and precisely what

functions and services an application can expect and an operating

system must offer the closest software equivalent to a hardware

bus specification. Each frees the user from the complexities of how

a program was written: forwhichsystem (portability) of whatsize

(scalability) or evenwhen(maintainability). The converse(interoperability) treats similar complexities for datarather than

programs.

In Open Systems, the celebrated seven-layer ISO Open Systems

Interconnect model guarantees to each level more critically, to

the programmers writing for it a consistent and relatively simple

interface to the level below it and above it and the right to ignore

everything else as if each lower level were well-behaved

hardware. The ultimate goal, particularly in the related areas ofmultiple-processor and parallel execution, is not only to free the user

from the complexity of wherein the network the processing occurs or

data resides, but from whichor how manyprocessors are used.

In Graphic User Interface Systems, the portability-scalability-

interoperability motif (lower case, again) is extended to managing the

complexity of the human interface:

a consistent look and feel makes expertise portableacross

applications and reduces to a humanly manageable number the

skills needed to use the system;

a progressive command and control interface makes skills

scalableacross levels of expertise, leading from simplicity for

the novice to power for the expert;


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a regular repertoire of data models makes information

interoperablebetween applications, from simple cut-and-paste

to sophisticated interchange formats;

a modular user interface makes systemsmaintainable,

presenting a fixed level of abstraction independent of the

underlying application, system or platform.

In Systems Integration, arguably the most lucrative opportunity in

the computer industry today, managing complexity is not the means

to an end it isthe end itself. As H. Ross Perot proved with EDS

and its billion-dollar facilities-management empire, To people with

headaches, sell aspirin. The standard dismissal of this opportunity

by manufacturing companies is that the field is a labor-intensive

Service industry with finicky customers, fickle employees, faulty

software and failure-prone hardware. But Perot realized that there

are economies of scale possible in serving a large number of similar

customers with fundamentally the same needs, that while it may not

be feasible economically to build a complex set of tools, procedures

and skills for one site, it may very well be practical to create such

products if amortized across many customers.

The RISC revolution offers a prime example of the turmoil of an

industry in transition ours. Indeed, the computer industry is naturally

fractious, both inter- and intra-company an understatement of the first

magnitude and this alone could almostaccount for the way in which

several large companies transition to RISC has nearly torn each of them

asunder. IBM lost savants Joel Birnbaum (to Hewlett-Packards RISC

design leadership), Glen Henry and Charlie Sauer (to Dell Computers

secret task force) and even team-leader Andy Heller (to Kleiner/Perkins

and even more secret services) and top manager Frank King (to Lotus).

Digital lost VMS-creator Dave Cutler (to Microsoft) in its internal RISC

wars, and the fabled loyalty of H-P customers was put to the ultimate test

in the years of delay for Spectrum/PA. Still, it appears that something

more fundamental is going on in the dark places of the mind.


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On Management

A slow sort of country! [said the Red Queen to Alice.] Here, you

see, it takes all the

running you can do just to keep in the same place. If you want to get

somewhere else,

you must run at least twice as fast as that!

Lewis Carrolls Through the Looking Glass 1872

Managing complexity seems to be the common thread weaving

together todays critical problems, the key issues of our industry

perhaps in all industries or perhaps in the world. If that sounds like a

grandiose, self-serving projection of our own concerns onto the face of the

planet, could not one legitimately define the failure of the Communist

system in Eastern Europe as a failure of central planning itself, an inability

to manage the intrinsic complexity? In the recent hostilities in Iraq, were

not the first targets attacked the command and control centers, the

enemys complexity management systems; were not a substantial

fraction of the Allied casualties from friendly fire a failure to manage

Wars incredible complexities?

From the Challenger disaster to the Hubble myopia, can we infer that

the complexity of coordinating the best and the brightest individual and

corporate talents is beyond NASA? Is not much of the yellow peril cited

by the American Automotive, Steel and even High-tech industries, our own

inability to match Japans uses of just-in-time inventory management, of

statistical quality control systems, of advanced manufacturing lines lending

themselves to robotics and automation, of dealer feedback systems tied

directly to manufacturing design?

Are these management problems an inevitable outcome of the

complexity of our age? Three classical approaches to reducing

complexity to manageable dimensions are neither unique to our times nor

to our industries, but are found in the works of three managers who


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operated in the complex and competitive environments of more than

twenty centuries past:

Divide and Conquer: Julius Caesar, inBella Gallica, begins his

classic description of the conquest of Gaul, ancient France, in 49 BC,with a simple declarative sentence: Gaul is divided into three parts.

Even today, satellite photography would not yield such an insight; it

was Caesar's understanding of the problem of conquering Gaul as

divisible into three simpler problems that was part of his managerial

genius.

Know Thy Adversary: Sun Tzu, inThe Art of War, his definitive

treatise of 202 BC, favors intelligence (military and otherwise) in

approaching the problem. He ranks an understanding of theenvironment weather, terrain and logistics as comparable in

strategic value to a knowledge of the strength and disposition of

opposing forces.

Keep It Simple & Straightforward: Alexander the Great, in legend,

resolved the complexity of theGordian Knotby the simplest

approach: a bold stroke of his sword. (It is rumored that even B-

school graduates learn some variant of the KISS acronym.)


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On RISC

RISC which is typically parsed as Reduced Instruction-Set

Computer

might be better understood if parsed as Reduced-Instruction-Set

Computer.

Dan Prenner (IBM, Yorktown Heights) 1989

RISC the technology of the Reduced Instruction Set Computer in its

various forms seems to have flashed upon the hardware talk circuit as

the hottest topic of recent years, generating at least as much fire and

smoke controversy and confusion as it has heat. This is not to saythat this new technology is not truly hot: measured in the rawMIPS

the equally ambiguous acronymic units of Millions of [computer]

Instructions Per Second RISC processors blaze past everything but the

supercomputers they internally resemble. Rather, it is that RISC,in and

of itself, is neither particularly new, novel nor even necessary except as

the latest solution to a recurring problem once again, managing

complexity.

Reduced Instruction-Set Complexity the more liberal interpretationespoused by one of IBMs RISC pioneers in the quote above is only the

most recent in a long sequence of hardwaresolutions to a problem of

softwarecomplexity management that seems to emerge inevitably once

a computer company reaches a critical mass or threshold complexity. At

the end of the 1950s, with a decade-and-a-halfs worth of mutually

incompatible system architectures and associated software to support,

IBM was the first to reach this threshold or at least the first to recognize

the problem and attempt to solve it. The solution began with an analysis

of the mix of machine instructions actually used in running programs

virtually the same analysis that led to RISC two decades later. Then,

however, the usage pie chart suggested a single, multipurpose, full-

circle (360-degree) virtual architecture. This democratic instruction set

was ultimately implemented on a whole family of machines of that name

by the all-to-common technique of each of the machines emulating the


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target instruction set but by design rather than default, using a control

store or microprogram.

At just about that time, Digital Equipment Corporation, lacking the

golden albatross the large installed base to service, loyal customersto support and market to cannibalize burst upon the scene with the

minicomputer. But by the end of their own decade and a half, with more

than a half-billion dollars in annual sales and more than 30,000 copies of

the PDP-8, PDP-11 and siblings installed worldwide, DEC similarly found

themselves with the unmanageable complexity of too many machine types

and operating systems. (The PDP-11 alone harbored RT-11, RSTS, RSX-

11D, RSX-11M and variants.) They, too, examined the instruction set and

leapt into the 32-bit domain with the VAX (Virtual Architecture eXtended)

the classicComplexInstruction-Set Computer. Arguably, of several

hundred proprietary CISC systems offered in 1976, only those of todays

first- and second-place manufacturers (the IBM 360 [extended] and Digital

VAX architectures) can survive into the next century.

While it was half a decade later that the RISC principles (and acronym)

were formally set forth by U.C. Berkeleys Glenn Patterson, the concepts

were not new: Digitals 12-bit PDP-8 minicomputer and, a decade later,

Intels 8-bit 8008 microcomputer each had an Instruction set reduced to fit

their tiny brains the same problem noted in Djikstras quote. At the

opposite end of the spectrum, supercomputer pioneer Seymour Cray had

stripped the Control Data 6600 and 7600 instruction sets of all

unnecessary complexities, facilitating instruction execution overlap

(better known today as pipelining) even before 1970. Thus by 1986, when

Hewlett-Packard had passed its own 15-year adolescence in the computer

industry, the young but proven RISC approach Spectrum or Precision

Architecture was the replacement of choice for the aging HP-1000,

-2000, and -3000 instruction sets.

Early that same year, RISC pioneer IBM fearing its golden albatross

might be blown off course by the gale-force processor born in IBM

temporary building 801, but wary of the rising winds of change

launched the RT/PC as a (safely underinflated) trial balloon into emerging

workstation markets. There it sank, hovered, drifted into commercial


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markets and ultimately rose above all but the very peaks of that terrain

but never quite reaching the lofty slope of ideal IBM sales curves. Soon

the semiconductor houses joined the fray, not only second-tier Fairchild

and AMD, but Intel and Motorola placed their dominant microprocessor-

specific market shares at RISC. In each case, whether RISC pioneer or

risk-averse follower, whether carried by covered wagon or bandwagon,

the companies involved were in their own eyes, at least

unquestionably hardwaremanufacturers. With the advent of Electronic

CAD and CAE (Computer-Aided-Design and -Engineering, respectively)

they now had tools to build tiny devices of incredible complexity and/or

speed, and each designer recognized the need to use it or lose it

competitively. But to a hardware designer that AND/OR was an illogic

gate, the near-mystical rate-limiting software bottleneck. To a hardwarevendor, the requisite softwareto managethis new hardware complexity

was not within their control possibly not in anyones control.

For despite the much-heralded birth of CASE (Computer-Aided

Software Engineering) as Mark Twain might have opined the rumors

of its birth were greatly exaggerated, the principal problems to be

solved were no longer in the clean and pure realm of hardwarebut

among the dark and murky black arts of software. The precipitating

rationale for the 85%-commercial/15%-technical IBM 360 family wasconceived, the original reason the ultra-CISC VAX was born and the initial

reason the H-P Spectrum saw the light of day was to solve what is actually

a software problem the never-ending saga and ever-increasing costs of

maintaining multiple OSs. Not surprisingly, the hardware vendors

solution was not to address the software complexity problem directly but to

move the complexity downward into a domain they didunderstand.

Following Henry Fords engineering solution to the one-color problem,

they slashed the Gordian knot of softwares uncertainty, complexity and

flexibility by moving it into the hardware.

Ironically, for well over a decade there had already been an alternative

solution that captured the very essence of the VLSI revolution, the

advantages of designing around standard, modular components

interconnected in a uniform and controlled fashion without nearly the loss


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of flexibility, expansibility and power. Today such approaches are called

open systemsor (less ambiguously) open software(lower case, please)

but at that time simply called the UNIXoperating system. Even more

ironically, the RISC revolution, which was largely a delayed but equal

and opposite reaction to the hardware overcomplication and speed limits

imposed by the CISC solution, pushed the responsibility for complexity

back into the software, specifically into the compiler, the unquestioned

forteof the UNIX OS.

Most ironically, the number-one proponent and provider of advanced

UNIX system software outside of Bell Laboratories by early 1988 was

next-generation enfant terribleSun Microsystems. Though barelysix

years old but already exceeding a billion dollars per year in revenue they

saw the impending catastrophe by the time they had begun to implement

their second machine architecture, the Intel-based 386i. In full

accordance with politically correct open thinking, they preemptively

developed and published the SPARCbinary design specification, a

general-purpose Scalable Processor Architecture instruction set, a RISC

for all reasons that everyone could (and properly should) employ, or so

they sincerely believed. The true irony is that this movemayvery well

have been a step backwards into the hardware solution by the most

successful champions of the UNIX operating system. Despitemisconceptions of UNIX systems being for engineers and scientists, the

UNIX forteis its standard arsenal of software tools compilers, editors,

analyzers, debuggers, specialized languages, utilities the best software

solutions to complex software problems.

A glib diagnosis of Suns apparent aberration would attribute it to PC

envy a morbid fixation on the market size of the IBM PC and its clones.

The difficulty arises not from simple ambition but from the single-minded

explanation of the PCs success almost entirely in terms of its plug-and-play, one-size-fits-all binary compatibility. This simplification manages to

neglect such factors as relative market sophistications (or the PC markets

lack thereof), total cost of ownership and usage or the level of complexity

appropriateness to the task at hand. Realistically, PCs are priced and

promoted as truly general-purpose devices with the all-too-subtle benefits


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of reducing complexity by reducing capability. When experienced

computer users purchase a system from supercomputer to workstation

they have a fairly good idea of the machines intended use. When a

naive user buys a PC, even a sophisticated corporate user may have only

a partial idea of the ends and purposes to which the PC will ultimately be

put. This market difference, more than any technical limitation,

determines that the majority of the PC software will be obtained in the

aftermarket, with the volumes characteristic of such products. A more

serious armchair psychoanalysis of the corporation if such a thing is

possible suggests that the corporate self-image is that of a hardware

company seeking hardwaresolutions wherever possible, even though fully

85 percent of the technical staff (and at least that fraction of the value-

added and differentiation from rival products) is insoftware.


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On Resolution

When you have eliminated the impossible, whatever remains,

however improbable,

must be the truth.

Mr. Sherlock Holmes in A. Conan Doyles The Sign of Four 1890

Our analysis suggests that perhaps the turbulence we are

experiencing signifies the boundary of a new domain, a new kind of

sound barrier, a transition from the classical physics of compressible

fluids, of the controllable complexity of the Hardware Paradigm. Far from

an impassable wall in the sky, the complexity barriermay be a gatewayto a new domain where the laws of nature are simply different not

better or worse. It may be that in this new domain complexities can no

longer be handled sequentially and linearly but must be solved in parallel

and nondeterministically, with all the uncertainty, chaos and surprise that

nonlinear systems imply. Thus when (notif) the function of software

today very much a service is sufficiently understood and sufficiently

valuedto be treated as a product as hardware is today then not

just the computer industry but the world itself may be a very different place

indeed. But where shall we find the key to this transformation? How will it

come about if we continue to solve the easy problems we understand

instead of the hard problems we dont? And most certainly, the software

problems are the hard problems. Just like the inebriate searching under

the lamppost for the keys lost in the bushes, we continue to look where

the light is better.

Ultimately, the economic and intellectual capital must be ventured, the

design tools sharpened, and the disciplines imposed. Butfirstthe

fundamental way we thinkabout software must undergo a transformation,

a paradigm shift back a dozen millennia, to before the comfortable, linear

predictable risk-averse world of agriculture and citification. We must

become familiar once again with the chaotic yet strangely attractive

world of the hunter-gatherer, the exploratory nature mapped into our

genes. Without yielding to fatalism or mysticism we may have to learn to


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accept the impossibility of absolute predictability once a critical level of

complexity is reached, to go with the flow of uncertainty rather than

attempting to conquer it. Even if we are uncomfortable with the idea of

God playing dice at the macroscopic level, we may need to forego strict

determinism as Djikstra proposes in his guarded command language

to give up procedurally instructing the machine how to accomplish our

goals, specifying only what objectives and endswe wish to accomplish,

leaving successively lower and decreasing levels of complexity to

determine the means. Whether achieved by by applicative fourth-

generation languages, by object-oriented programming techniques, by

standardized application, intersystem and human interfaces or (most

likely) all of the above combined with ideas yet undreamed, we need to

learn the larger lesson inherent in the RISC phenomenon and the X-windows phenomenon and the Open Systems and Software phenomena

as applied to managing complexity.


23/24

That lesson is that, increasingly, it is insufficient to escape from the

linear only to project our trend lines into a geometric or even an

exponential future. We must make a fundamental shift in our thinking, its

dimension, if we are to have any hope of capturing the metatrendsthat

allow us to stand on the shoulders rather than the toes of our intellectual

forebears. One examination of a trend in our own industry should at least

illustrate this concept. Consider, literally, the evolution indimensionof

the relation between human and computer:

Dimension 0: (circa 1960): apoint punched in a card in a deck

slipped through the slot to a white-robed high priest of the batch-

oriented mainframe, responding to ones JCL commands in hours

or days and using a simple linear file system;

Dimension 1: (circa 1970): aline of text typed interactively on a

Teletype on-line to a time-shared minicomputer with response in

seconds or minutes, a command-lineinterpreter, a simple line-

editor and a more powerful hierarchical database;

Dimension 2: (circa 1980): ascreen of text randomly accessible

on a single-task personal computer with a response in fractions of

seconds or seconds, a menu-bar command structure, character-

oriented word processor and relational database;

Dimension 3: (circa 1990): multiple overlapped windows of

graphics and text on a multitasking workstation with a response in

milliseconds, an object-oriented icon-based command

structure, a WYSIWYG desktop publishing system and an

entity-object database seamlessly incorporating text, graphics and

images;

Dimension 4: (circa 2000): ...left as an exercise for the readers

own capabilities...


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Dr. Brian Boyle, currently Director of Research for NOVON Research

Group in Berkeley, California, holds an MD (Board Certified in Radiology,

Specialty in Nuclear Medicine and Medical Imaging,) and a PhD in

Medical Information Science from University of California, San Francisco,

an MSEE in Computer Science from the U.C, Berkeley, and a BS in

Biophysics and in Experimental Psycholgy from Harvey Mudd College.

Prior to founding NOVON in 1980 to investigate prospects for distributed

Non-Von Neumann architectures, Dr. Boyle held positions in academia

and industry and consulted for the scientific, commercial and government

sectors. As Manager of Varian Associates Instrument Data Systems

Division, he has worked with the UNIX Operating System since 1974.

He investigated its usage and future managing the Systems and Software

Group of McGraw-Hill's high-technology market research arm until 1985,when he returned to NOVON for its independent status as a research,

consulting and publishing organization with the charter of exploring the

next generation of systems and software technologies. An active member

of the ACM, AAMI, IDBMA (Pick OS), IEEE, Usenix and Uniforum, he has

participated in the creation of the IEEE 1003 POSIX standard since 1981

and founded (in 1983) the Uniforum Internationalization Technical

Advisory Committee, efforts he continues today as a frequent invited

speaker at academic, technical and trade conferences worldwide.

risc+reward_1988

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