Chapter 1
Introduction
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Chapter 1 Objectives
• Know the difference between computer organization
and computer architecture.
• Understand units of measure common to computer
systems.
• Understand the evolution of computers.
• Understand the computer as a layered system.
• Be able to explain the von Neumann architecture and
the function of basic computer components.
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Why study computer organization and
architecture?
– Design better programs, including system
software such as compilers, operating systems,
and device drivers.
– Optimize program behavior.
– Evaluate (benchmark) computer system
performance.
– Understand time, space, and price tradeoffs.
1.1 Overview
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1.1 Overview
• Computer organization
– Encompasses all physical aspects of computer
systems.
– E.g., circuit design, control signals, memory types.
– How does a computer work?
• Computer architecture
– Logical aspects of system implementation as seen by the
programmer.
– E.g., instruction sets, instruction formats, data types,
addressing modes.
– How do I design a computer?
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1.2 Computer Components
• There is no clear distinction between matters
related to computer organization and matters
relevant to computer architecture.
• Principle of Equivalence of Hardware and
Software:
– Any task done by software can also be done using
hardware
– Any operation performed directly by hardware can
be done using software (assuming speed is not an
issue)
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• At the most basic level, a computer is a
device consisting of three pieces:
– A processor to interpret and execute programs
– A form of memory to store both data and
programs
– A mechanism for transferring data to and from
the outside world.
1.2 Computer Components
Questions?
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1.3 An Example System
What does it all mean??
Consider this advertisement:
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SI Prefixes for larger numbers (Storage and Speed):
• Kilo- (K) = 1 thousand = 103 and 210
• Mega- (M) = 1 million = 106 and 220
• Giga- (G) = 1 billion = 109 and 230
• Tera- (T) = 1 trillion = 1012 and 240
• Peta- (P) = 1 quadrillion = 1015 and 250
• Exa- (E) = 1 quintillion = 1018 and 260
• Zetta- (Z) = 1 sextillion = 1021 and 270
• Yotta- (Y) = 1 septillion = 1024 and 280
1.3 An Example System
Whether a metric refers to a power of ten or a power of
two typically depends upon what is being measured.
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1.3 An Example System
SI Prefixes for smaller numbers (Physical Size):
• Milli- (m) = 1 thousandth = 10 -3
• Micro- () = 1 millionth = 10 -6
• Nano- (n) = 1 billionth = 10 -9
• Pico- (p) = 1 trillionth = 10 -12
• Femto- (f) = 1 quadrillionth = 10 -15
• Atto- (a) = 1 quintillionth = 10 -18
• Zepto- (z) = 1 sextillionth = 10 -21
• Yocto- (y) = 1 septillionth = 10 -24
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• Bit = the smallest unit of storage
• Byte = a common unit of storage
– 1 Byte = 8 bits
– 1 KB = 210 = 1024 Bytes
– 1 MB = 220 = 1,048,576 Bytes
– 1 GB = 230 = 1,073,741,824 Bytes
– 1 TB = 240 = 1,099,511,627,776 Bytes
– Main memory (RAM) is measured in MB or GB
– Disk storage is measured in GB or TB
• A Bit is usually represented as lowercase b
• A Byte is usually represented as an uppercase B
1.3 An Example System
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• Hertz = unit of frequency (operations per second)
– 1MHz = 1,000,000Hz
– Processor speeds are measured in MHz or GHz.
• Second = unit of time
– Hard disk drive access times are often 10 to 20 milliseconds.
– Main memory access times are often 50 to 70 nanoseconds.
• Meter = unit of size
– Micron (micrometer) = 1 millionth of a meter
– Circuits on computer chips are measured in microns
1.3 An Example System
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• We note that cycle time is the reciprocal of clock
frequency.
• A bus operating at 133MHz has a cycle time of
7.52 nanoseconds:
1.3 An Example System
Now back to the advertisement …
133,000,000 cycles/second = 7.52ns/cycle
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1.3 An Example System
Consider this advertisement:
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1.3 An Example System
• Computers with a large main memory capacity
can run largers programs or programs working
on larger data sets at greater speeds than
computers having smaller memories.
• RAM is an acronym for random access
memory. Random access means that memory
contents can be accessed directly if you know its
location.
• RAM typically refers to main memory. Cache is a
type RAM that can be accessed faster than main
memory.
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1.3 An Example System
• Serial ports send data as a series of pulses along
one or two data lines.
• Parallel ports send data as a single pulse along
at least eight data lines.
• USB, Universal Serial Bus, is an intelligent serial
interface that is self-configuring. (It supports
“plug and play.”)
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Throughout the remainder of the course, you
will see how these components work and how
they interact with software to make complete
computer systems.
This statement raises two important questions:
What assurance do we have that computer
components will operate as we expect?
And what assurance do we have that
computer components will operate together?
1.3 An Example System
Questions?
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• There are many organizations that set
computer hardware standards– to include
the interoperability of computer components.
• Throughout this course, and in your career,
you will encounter many of them.
• Some of the most important standards-
setting groups are . . .
1.4 Standards Organizations
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• The Institute of Electrical and Electronic
Engineers (IEEE)
– Promotes the interests of the worldwide
electrical engineering community.
– Establishes standards for computer components,
data representation, and signaling protocols,
among many other things.
1.4 Standards Organizations
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• The International Telecommunications Union
(ITU)
– Concerns itself with the interoperability of
telecommunications systems, including data
communications and telephony.
• National groups establish standards within their
respective countries:
– The American National Standards Institute (ANSI)
– ECMA International
– The British Standards Institution (BSI)
1.4 Standards Organizations
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• The International Organization for
Standardization (ISO)
– Establishes worldwide standards for everything
from screw threads to photographic film.
– Is influential in formulating standards for
computer hardware and software, including their
methods of manufacture.
Note: ISO is not an acronym. ISO comes from the Greek,
isos, meaning “equal.”
1.4 Standards Organizations
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• To fully appreciate the computers of today, it is
helpful to understand how things got the way they
are.
• The evolution of computing machinery has taken
place over several centuries.
• In modern times computer evolution is usually
classified into four generations according to
technology of the era.
We note that many of the following dates are approximate.
1.5 Historical Development
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• Generation Zero: Mechanical Calculating Machines
(1642 – 1945)
– Calculating Clock – Wilhelm Schickard (1592 – 1635).
– Pascaline – Blaise Pascal (1623 – 1662).
– Difference Engine – Charles Babbage (1791 – 1871), also
designed but never built the Analytical Engine.
– Punched card tabulating machines – Herman Hollerith
(1860 – 1929).
Hollerith cards were commonly used for
computer input well into the 1970s.
1.5 Historical Development
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• The First Generation: Vacuum Tube Computers (1945 –
1953)
– Atanasoff Berry Computer (1937 –
1938) solved systems of linear
equations.
– John Atanasoff and Clifford Berry of
Iowa State University.
1.5 Historical Development
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• The First Generation: Vacuum Tube Computers
(1945 – 1953)
– Electronic Numerical Integrator and
Computer (ENIAC)
– John Mauchly and J. Presper Eckert
– University of Pennsylvania, 1946
• The ENIAC was the first general-purpose
computer.
1.5 Historical Development
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• The First Generation: Vacuum Tube Computers
(1945 – 1953)
– The IBM 650 first mass-produced computer. (1955)
° It was phased out in 1969.
– Other major computer manufacturers of this period
include UNIVAC, Engineering Research Associates
(ERA), and Computer Research Corporation (CRC).
° UNIVAC and ERA were bought by Remington Rand, the
ancestor of the Unisys Corporation.
° CRC was bought by the Underwood (typewriter)
Corporation, which left the computer business.
1.5 Historical Development
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• The Second Generation: Transistorized
Computers (1954 – 1965)
– IBM 7094 (scientific) and 1401 (business)
– Digital Equipment Corporation (DEC) PDP-1
– Univac 1100
– Control Data Corporation 1604.
– . . . and many others.
1.5 Historical Development
These systems had few architectural similarities.
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• The Third Generation: Integrated Circuit
Computers (1965 – 1980)
– IBM 360
– DEC PDP-8 and PDP-11
– Cray-1 supercomputer
– . . . and many others.
• By this time, IBM had gained overwhelming
dominance in the industry.
– Computer manufacturers of this era were characterized
as IBM and the BUNCH (Burroughs, Unisys, NCR,
Control Data, and Honeywell).
1.5 Historical Development
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• The Fourth Generation: VLSI Computers
(1980 – ????)
– Very large scale integrated circuits (VLSI) have more
than 10,000 components per chip.
– Enabled the creation of microprocessors.
– The first was the 4-bit Intel 4004.
– Later versions, such as the 8080, 8086, and 8088
spawned the idea of “personal computing.”
1.5 Historical Development
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• Moore’s Law (1965)
– Gordon Moore, Intel founder
– “The density of transistors in an integrated circuit will
double every year.”
• Contemporary version:
– “The density of silicon chips doubles every 18 months.”
But this “law” cannot hold forever …
1.5 Historical Development
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• Rock’s Law
– Arthur Rock, Intel financier
– “The cost of capital equipment to build semiconductors
will double every four years.”
– In 1968, a new chip plant cost about $12,000.
At the time, $12,000 would buy a nice home in
the suburbs.
An executive earning $12,000 per year was
“making a very comfortable living.”
1.5 Historical Development
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• Rock’s Law
– In 2012, a chip plants under construction cost well
over $5 billion.
– For Moore’s Law to hold, Rock’s Law must fall, or
vice versa. But no one can say which will give out
first.
$5 billion is more than the gross domestic
product of some small countries, including
Barbados, Mauritania, and Rwanda.
1.5 Historical Development
Questions?
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• Computers consist of many things besides
chips.
• Before a computer can do anything worthwhile,
it must also use software.
• Writing complex programs requires a “divide
and conquer” approach, where each program
module solves a smaller problem.
• Complex computer systems employ a similar
technique through a series of virtual machine
layers.
1.6 The Computer Level Hierarchy
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• Each virtual machine layer
is an abstraction of the level
below it.
• The machines at each level
execute their own particular
instructions, calling upon
machines at lower levels to
perform tasks as required.
• Computer circuits ultimately
carry out the work.
1.6 The Computer Level Hierarchy
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• Level 6: The User Level
– Program execution and user interface level.
– The level with which we are most familiar.
• Level 5: High-Level Language Level
– The level with which we interact when we write
programs in languages such as C, Pascal, Lisp, and
Java.
1.6 The Computer Level Hierarchy
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• Level 4: Assembly Language Level
– Acts upon assembly language produced from
Level 5, as well as instructions programmed
directly at this level.
• Level 3: System Software Level
– Controls executing processes on the system.
– Protects system resources.
– Assembly language instructions often pass
through Level 3 without modification.
1.6 The Computer Level Hierarchy
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• Level 2: Machine Level
– Also known as the Instruction Set Architecture
(ISA) Level.
– Consists of instructions that are particular to the
architecture of the machine.
– Programs written in machine language need no
compilers, interpreters, or assemblers.
1.6 The Computer Level Hierarchy
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• Level 1: Control Level
– A control unit decodes and executes instructions
and moves data through the system.
– Control units can be microprogrammed or
hardwired.
– A microprogram is a program written in a low-
level language that is implemented by the
hardware.
– Hardwired control units consist of hardware that
directly executes machine instructions.
1.6 The Computer Level Hierarchy
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• Level 0: Digital Logic Level
– This level is where we find digital circuits (the
chips).
– Digital circuits consist of gates and wires.
– These components implement the mathematical
logic of all other levels.
1.6 The Computer Level Hierarchy
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• The ultimate aim of every computer system is
to deliver functionality to its users.
• Computer users typically do not care about
terabytes of storage and gigahertz of processor
speed.
• Many companies outsource their data centers
to third-party specialists, who agree to provide
computing services for a fee.
• These arrangements are managed through
service-level agreements (SLAs).
1.7 Computing as a Service: Cloud Computing
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• Rather than pay a third party to run a
company-owned data center, another
approach is to buy computing services from
someone else’s data center and connect to it
via the Internet.
• This is the idea behind a collection of service
models known as Cloud computing.
1.7 Computing as a Service: Cloud Computing
The “Cloud” is a visual metaphor traditionally
used for the Internet. It is even more apt tor
service-defined computing.
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• More Cloud computing models:
– Software as a Service, or SaaS. The consumer of this
service buy application services
• Well-known examples include Gmail, Google Docs,
Dropbox, Zoom, and Netflix.
– Platform as a Service, or PaaS. Provides server
hardware, operating systems, database services,
security components, and backup and recovery
services.
• Well-known PaaS providers include Google App Engine
and Microsoft Windows Azure Cloud Services.
1.7 Computing as a Service: Cloud Computing
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• The general term, Cloud computing, consists of
several models:
– Infrastructure as a Service (IaaS) provides only server
hardware, secure network access to the servers, and backup
and recovery services. The customer is responsible for all
system software including the operating system and
databases.
• Well-known IaaS platforms include Amazon EC2,
Google Compute Engine, Microsoft Azure Services
Platform, Rackspace, and HP Cloud.
1.7 Computing as a Service: Cloud Computing
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• More Cloud computing models:
– Infrastructure as a Service (IaaS) provides only server
hardware, secure network access to the servers, and backup
and recovery services. The customer is responsible for all
system software including the operating system and
databases.
• Well-known IaaS platforms include Amazon EC2,
Google Compute Engine, Microsoft Azure Services
Platform, Rackspace, and HP Cloud.
– Cloud storage is a limited type of IaaS that includes services
such as Dropbox, Google Drive, and Amazon.com’s Cloud
Drive.
1.7 Computing as a Service: Cloud Computing
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• Cloud computing relies on the concept of
elasticity where resources can be added and
removed as needed.
• You pay for only what you use.
• Virtualization is an enabler of elasticity.
– Instead of having a physical machine, you have a
“logical” machine that may span several physical
machines, or occupy only part of a single physical
machine.
1.7 Computing as a Service: Cloud Computing
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• On the ENIAC, all programming was done at
the digital logic level.
• Programming the computer involved moving
plugs and wires.
• A different hardware configuration was needed
to solve every unique problem type.
1.8 The von Neumann Model
Configuring the ENIAC to solve a “simple” problem
required many days labor by skilled technicians.
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• Inventors of the ENIAC, John Mauchley and
J. Presper Eckert, conceived of a computer
that could store instructions in memory.
• The invention of this idea has since been
ascribed to a mathematician, John von
Neumann, who was a contemporary of
Mauchley and Eckert.
• Stored-program computers have become
known as von Neumann Architecture systems.
1.8 The von Neumann Model
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• Today’s stored-program computers have the
following characteristics:
– Three hardware systems:
• A central processing unit (CPU)
• A main memory system
• An I/O system
– The capacity to carry out sequential instruction
processing.
– A single data path between the CPU and main memory.
• This single path is known as the von Neumann
bottleneck.
1.8 The von Neumann Model
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• This is a general
depiction of a von
Neumann system:
• These computers
employ a fetch-
decode-execute
cycle to run
programs as
follows . . .
1.8 The von Neumann Model
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• The control unit fetches the next instruction from memory using
the program counter to determine where the instruction is
located.
1.8 The von Neumann Model
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• The instruction is decoded into a language that the ALU
can understand.
1.8 The von Neumann Model
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• Any data operands required to execute the instruction
are fetched from memory and placed into registers
within the CPU.
1.8 The von Neumann Model
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• The ALU executes the instruction and places results in
registers or memory.
1.8 The von Neumann Model
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• Conventional stored-program computers have
undergone many incremental improvements over
the years.
• These improvements include adding specialized
buses, floating-point units, and cache memories,
to name only a few.
• But enormous improvements in computational
power require departure from the classic von
Neumann architecture.
• Adding processors is one approach.
1.9 Non-von Neumann Models
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• Some of today’s systems have separate buses for
data and instructions.
– Called a Harvard architecture
• Other non-von Neumann systems provide special-
purpose processors to offload work from the main
CPU.
• More radical departures include dataflow
computing, quantum computing, cellular
automata, and parallel computing.
1.9 Non-von Neumann Models
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• In the late 1960s, high-performance computer
systems were equipped with dual processors to
increase computational throughput.
• In the 1970s supercomputer systems were
introduced with 32 processors.
• Supercomputers with 1,000 processors were built
in the 1980s.
• In 1999, IBM announced its Blue Gene system
containing over 1 million processors.
1.10 Parallel Computing
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• Parallel processing allows a computer to
simultaneously work on subparts of a problem.
• Multicore processors have two or more processor
cores sharing a single die.
• Each core has its own ALU and set of registers,
but all processors share memory and other
resources.
• “Dual core” differs from “dual processor.”
– Dual-processor machines, for example, have two
processors, but each processor plugs into the
motherboard separately.
1.10 Parallel Computing
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• Multi-core systems provide the ability to run
multiple tasks at the same time.
• Multithreaded applications take advantage of
multi-core systems by being programmed to run
multiple “threads” of the program at the same
time.
• New programming languages are often necessary
to fully exploit multiprocessor power.
1.10 Parallel Computing
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• The quest for machine intelligence has been
ongoing for over 300 years.
• The 20th Century witnessed the first machines that
could be human grandmasters at chess when
Deep Blue beat Garry Kasparov in 1997.
• But the machine and the algorithm relied on a
brute force solution, although impressive, hardly
“intelligent” by any measure.
1.11 Parallelism: Enabler of Machine Intelligence
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• Any definition of true machine “intelligence” would
have to include the ability to acquire new
knowledge independent of direct human
intervention, and the ability to solve problems
using incomplete and perhaps contradictory
information.
• This is precisely what IBM achieved when it build
the machine named Watson.
• Watson proved this when it beat two human
Jeopardy ! champions on February 16, 2011.
1.11 Parallelism: Enabler of Machine Intelligence
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• Watson had a massively parallel architecture
dubbed DeepQA (Deep Question and Answer).
• The system relied on 90 IBM POWER 750
servers.
• Each server was equipped with four POWER7
processors, and each POWER7 processor had
eight cores, giving a total of 2880 processor
cores.
• While playing Jeopardy!, each core had access to
16TB of main memory and 4TB of storage.
1.11 Parallelism: Enabler of Machine Intelligence
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• Watson’s technology has been put to work in
treating cancer.
– Commercial products based on Watson technology,
including “Interactive Care Insights for Oncology”
and “Interactive Care Reviewer,” are now available.
• Watson is also becoming more compact: Watson
can now be run on a single POWER 750 server.
• Watson has surely given us a glimpse into the
future of computing.
1.11 Parallelism: Enabler of Machine Intelligence
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• This chapter has given you an overview of the
subject of computer architecture.
• You should now be sufficiently familiar with
general system structure to guide your studies
throughout the remainder of this course.
• Subsequent chapters will explore many of these
topics in great detail.
Conclusion