Chapter 1 Fundamentals of Computer Design

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Chapter 1Fundamentals of Computer Design
EEF011 Computer Architecture?????

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• September 2004

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Chapter 1. Fundamentals of Computer Design

• 1.1 Introduction
• 1.2 The Changing Face of Computing and the Task
  of the Computer Designer
• 1.3 Technology Trends
• 1.4 Cost, Price, and Their Trends
• 1.5 Measuring and Reporting Performance
• 1.6 Quantitative Principles of Computer Design
• 1.7 Putting It All Together
• 1.8 Another View
• 1.9 Fallacies and Pitfalls
• 1.10 Concluding Remarks

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Computer Architecture 1.1 Introduction

• Rapid rate of improvement in the roughly 55 years
• the technology used to build computers
• innovation in computer design
• Beginning in about 1970,computer designers became
  largely dependent upon IC technology
• Emergence of microprocessor late 1970s
• C virtual elimination of assembly language
  programming (portability)
• UNIX standardized, vendor-independent OS
• Lowered the cost and risk of bringing out a new
  architecture

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1.1 Introduction (cont.)

• Development of RISC (Reduced Instruction Set
  Computer) early 1980s
• Focused the attention of designers on
• The exploitation of instruction-level parallelism
• Initially through pipelining, and
• later through multiple instruction issue
• The use of caches
• initially in simple forms and
• later using more sophisticated organizations and
  optimizations

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Figure 1.1 20 years of sustained growth in
performance at an annual rate of over 50 (two
different versions of SPEC, i.e., SPEC92 and
SPEC95)

• Effects
• Significantly enhanced the capability available
  to users
• Dominance of microprocessor-based computers
  across the entire range of computer design
• Minicomputers and mainframes replaced
• Supercomputers built with collections of
  microprocessors

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1.2 Changing Face of Computing

• 1960s dominated by mainframes
• business data processing, large-scale scientific
  computing
• 1970s the birth of minicomputers
• scientific laboratories, time-sharing
  multiple-user environment
• 1980s the rise of the desktop computer (personal
  computer and workstation)
• 1990s the emergence of the Internet and the
  World Wide Web
• handheld computing devices
• emergence of high-performance digital consumer
  electronics

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Segmented Markets

• Desktop computing to optimize price-performance
• the largest market in dollar terms
• Web-centric, interactive applications
• Servers
• Web-based services, replacing traditional
  mainframe
• Availability, Scalability, Throughput
• Embedded Computers
• Features
• The presence of the computers is not immediately
  obvious
• The application can usually be carefully tuned
  for the processor and system
• Widest range of processing power and cost
• price is a key factor in the design of computers
• Real-time performance requirement
• Key characteristics
• the need to minimize memory and power
• The use of processor cores together with
  application-specific circuitry

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Task of Computer Designer

• Tasks
• Determine what attributes are important for a new
  machine, then
• design a machine to maximize performance while
  staying within cost and power constraints
• Efforts
• instruction set design, functional organization,
  logic design, and implementation (IC design,
  packaging, power, and cooling)
• Design a computer to meet functional requirements
  as well as price, power, and performance goals
• (see Figure 1.4)

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Task of a Computer Designer
Effect of trends Designer often needs to design
for the next technology because during a single
product cycle (typically 4-5 years), key
technologies, such as memory, change sufficiently
that the designer must plan for these changes.
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1.3 Technology Trends

• The designer must be especially aware of rapidly
  occurring changes in implementation technology
• Integrated circuit logic technology
• transistor density increases by about 35 per
  year
• die size are less predictable and slower
• combined effect growth rate in transistor count
  about 55 per year
• Semiconductor DRAM
• density increases by between 40 and 60 per year
• cycle time decreased by about one-third in 10
  years
• bandwidth per chip increases about twice
• Magnetic disk technology
• Network technology

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Moores Law

• Gordon Moore (Founder of Intel) observed in 1965
  that the number of transistors that could be
  crammed on a chip doubles every year.
• http//www.intel.com/research/silicon/mooreslaw.h
  tm

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Technology Trends

• Based on SPEED, the CPU has increased
  dramatically, but memory and disk have increased
  only a little. This has led to dramatic changed
  in architecture, Operating Systems, and
  Programming practices.

Capacity Speed (latency) Logic 2x in 3
years 2x in 3 years DRAM 4x in 3 years 2x in
10 years Disk 4x in 3 years 2x in 10 years
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1.4 Cost, Price, and Their Trends

• Price what you sell a finished good
• 1999 more than half the PCs sold were priced at
  less than 1000
• Cost the amount spent to produce it, including
  overhead

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Cost and Its Trend

• Time
• Learning curve manufacturing cost reduces over
  time
• Yield the percentage of the manufactured devices
  that survive the testing procedure
• As the technology matures over time, the yield
  improves and hence, things get cheaper
• Volume
• Increasing volume decreases cost (time for
  learning curve),
• Increases purchasing and manufacturing efficiency
• Commodities
• Products are sold by multiple vendors in large
  volumes and are essentially identical
• The low-end business such as DRAMs, disks,
  monitors
• Improved competition ? reduced cost

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Prices of six generations of DRAMs
Between the start of a project and the shipping
of a produce, say, two years, the cost of a new
DRAM drops by a factor of between 5 and 10 in
constant dollars.
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Price of an Intel Pentium III at a Given Frequency
It decreases over time as yield enhancements
decrease the cost of a good die and competition
forces price reductions.
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Cost and Its Trend (Cont.)

• Time
• Learning curve manufacturing cost reduces over
  time
• Yield the percentage of the manufactured devices
  that survive the testing procedure
• As the technology matures over time, the yield
  improves and hence, things get cheaper
• Volume
• Increasing volume decreases cost (time for
  learning curve),
• Increases purchasing and manufacturing efficiency
• Commodities
• Products are sold by multiple vendors in large
  volumes and are essentially identical
• The low-end business such as DRAMs, disks,
  monitors
• Improved competition ? reduced cost

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Wafer and Dies
Exponential cost decrease technology basically
the same A wafer is tested and chopped into dies
that are packaged
Figure 1.8. This wafer contains 564 MIPS64 R20K
processors in 0.18? Figure 1.7. Intel Pentium 4
microprocessor Die
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Cost of an Integrated Circuit (IC)

• Cost of IC

(A greater portion of the cost that varies
between machines)
(sensitive to die size)
of dies along the edge
Todays technology ? ? 4, detect density 0.4 and
0.8 per cm2
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Cost of an IC (cont.)
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Cost Example in a 1000 PC in 2001
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1.5 Measuring and Reporting Performance

• Designing high performance computers is one of
  the major goals of any computer architect.
• As a result, assessing the performance of
  computer hardware is the at the heart of computer
  design and greatly affects the demand and market
  value of the computer.
• However, measuring performance of a computer
  system is not a straightforward task
• Metrics How do we describe in a numerical way
  the performance of a computer?
• What tools do we use to find those metrics?
• How do we summarize the performance?

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What is the computer user interested in?

• Reduce the time to run certain task
• Execution time (response time)
• The time between the start and the completion of
  an event
• Increase the tasks per week, day, hour, sec, ns
  
• Throughput
• The total amount of work done in a given time

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Example

• Do the following changes to a computer system
  increase throughput, reduce response time, or
  both?
• Replacing the processor in a computer with a
  faster version
• Adding additional processors to a system that
  uses multiple processors for separate tasks for
  example handling an airline reservation system

• Answer
• Both response time and throughput are improved
• Only throughput increases

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Execution Time and Performance Comparison

• In this subject, we will be primarily interested
  in execution time as a measure of performance
• The relationship between performance and
  execution time on a computer X (reciprocal) is
  defined as follows
• To compare design alternatives, we use the
  following equation
• X is n times faster than Y or the throughput
  of X is n times higher than Y means that the
  execution time is n times less on X than Y
• To maximize performance of an application, we
  need to minimize its execution time

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Measure Performance user CPU time

• Response time may include disk access, memory
  access, input/output activities, CPU event and
  operating system overhead - everything
• In order to get an accurate measure of
  performance, we use CPU time instead of using
  response time
• CPU time is the time the CPU spends computing a
  program and does not include time spent waiting
  for I/O or running other programs
• CPU time can also be divided into user CPU time
  (program) and system CPU time (OS)
• Key in UNIX command time, we have,
• 90.7u 12.9s 2.39 65 (user CPU, system CPU,
  total response,)
• In our performance measures, we use user CPU time
  because of its independence on the OS and other
  factors

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Programs for Measuring Performance

• Real applications text processing software
  (Word), compliers (C), and other applications
  like Photoshop have inputs, outputs, and
  options when the user wants to use them
• One major downside Real applications often
  encounter portability problems arising from
  dependences on OS or complier
• Modified (or scripted) applications Modification
  is to enhance portability or focus on one
  particular aspect of system performance. Scripts
  are used to simulate the interaction behaviors
• Kernels small, key pieces from real programs.
  Typically used to evaluate individual features of
  the machine
• Toy benchmarks typically between 10 and 100
  lines of code and produce a known result
• Synthetic benchmarks artificially created code
  to match an average execution profile

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Benchmark Suites

• They are a collection of programs (workload) that
  try to explore and capture all the strengths and
  weaknesses of a computer system (real programs,
  kernels)

• A key advantage of such suites is that the
  weakness of any one benchmark is lessened by the
  presence of the other benchmarks
• Good vs. Bad benchmarks
• Improving product for real programs vs. improving
  product for benchmarks to get more sales
• If benchmarks are inadequate, then sales wins!

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SPEC Benchmarks
SPEC Standard Performance Evaluation Cooperation

• Most successful attempts and widely adopted
• First generation 1989
• 10 programs yielding a single number
  (SPECmarks)
• Second generation 1992
• SPECInt92 (6 integer programs) and SPECfp92 (14
  floating point programs)
• Unlimited compiler flags
• Third generation 1995
• New set of programs SPECint95 (8 integer
  programs) and SPECfp95 (10 floating point)
• Single flag setting for all programs
  SPECint_base95, SPECfp_base95
• benchmarks useful for 3 years
• SPEC CPU 2000
• CINT2000 (11 integer programs) andCFP2000 (14
  floating point programs)

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SPEC Benchmarks
CINT2000 (Integer Component of SPEC CPU2000)

• Program Language What Is It
• 164.gzip C Compression
• 175.vpr C FPGA Circuit Placement and Routing
• 176.gcc C C Programming Language Compiler
• 181.mcf C Combinatorial Optimization
• 186.crafty C Game Playing Chess
• 197.parser C Word Processing
• 252.eon C Computer Visualization
• 253.perlbmk C PERL Programming Language
• 254.gap C Group Theory, Interpreter
• 255.vortex C Object-oriented Database
• 256.bzip2 C Compression
• 300.twolf C Place and Route Simulator

http//www.spec.org/osg/cpu2000/CINT2000/
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SPEC Benchmarks
CFP2000 (Floating Point Component of SPEC CPU2000)

• Program Language What Is It
• 168.wupwise Fortran 77 Physics / Quantum
  Chromodynamics
• 171.swim Fortran 77 Shallow Water Modeling
• 172.mgrid Fortran 77 Multi-grid Solver 3D
  Potential Field
• 173.applu Fortran 77 Parabolic / Elliptic
  Differential Equations
• 177.mesa C 3-D Graphics Library
• 178.galgel Fortran 90 Computational Fluid
  Dynamics
• 179.art C Image Recognition / Neural Networks
• 183.equake C Seismic Wave Propagation Simulation
• 187.facerec Fortran 90 Image Processing Face
  Recognition
• 188.ammp C Computational Chemistry
• 189.lucas Fortran 90 Number Theory / Primality
  Testing
• 191.fma3d Fortran 90 Finite-element Crash
  Simulation
• 200.sixtrack Fortran 77 High Energy Physics
  Accelerator Design
• 301.apsi Fortran 77 Meteorology Pollutant
  Distribution

http//www.spec.org/osg/cpu2000/CFP2000/
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More Benchmarks
TPC Transaction Processing Council

• Measure the ability of a system to handle
  transactions, which consist of database accesses
  and updates.
• Many variants depending on transaction complexity
• TPC-A simple bank teller transaction style
• TPC-C complex database query

EDN Embedded Microprocessor Benchmark Consortium
(EEMBC, embassy)

• 34 kernels in 5 classes
• 16 automotive/industrial 5 consumer 3
  networking 4 office automation 6
  telecommunications

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How to Summarize Performance

• Management would like to have one number
• Technical people want more
• They want to have evidence of reproducibility
  there should be enough information so that you or
  someone else can repeat the experiment
• There should be consistency when doing the
  measurements multiple times

How would you report these results?
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Comparing and Summarizing Performance

• Comparing the performance by looking at
  individual programs is not fair
• Total execution time a consistent summary
  measure
• Arithmetic Mean provides a simple average
• Timei execution time for program i in the
  workload
• Doesnt account for weight all programs are
  treated equal

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Weighted Variants

• What is the proper mixture of programs for the
  workload?
• Weight is a weighting factor to each program to
  indicate the relative frequency of the program in
  the workload of use
• Weighted Arithmetic Mean
• Weighti frequency of program i in the workload
• May be better but beware the dominant program
  time

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Example Figure 1.16
Weighted Arithmetic Mean
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Normalized Time Metrics

• Normalized execution time metrics
• Measure the performance by normalizing it to a
  reference machine Execution time ratioi
• Geometric Mean
• Geometric mean is consistent no matter which
  machine is the reference
• The arithmetic mean should not be used to average
  normalized execution times
• However, geometric mean still doesnt form
  accurate predication models (doesnt predict
  execution time). It encourages designers to
  improve the easiest benchmarks rather the slowest
  benchmarks (due to no weighting)

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Example
Machines A and B have the same performance
according to the Geometric Mean measure, yet this
would only be true for a workload that P1 runs
100 times more than P2 according to the Weighted
Arithmetic Mean measure
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1.6 Quantitative Principles of Computer Design

• Already known how to define, measure and
  summarize performance, then we can explore some
  of the principles and guidelines in design and
  analysis of computers.
• Make the common case fast
• In making a design trade-off, favor the frequent
  case over the infrequent case
• Improving the frequent event, rather than the
  rare event, will obviously help performance
• Frequent case is often simpler and can be done
  faster than the infrequent case
• We have to decide what the frequent case is and
  how much performance can be improved by making
  the case faster

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Two equations to evaluate design alternatives

• Amdahls Law

• Amdahls Law states that the performance
  improvement to be gained from using some fast
  mode of execution is limited by the fraction of
  the time the faster mode can be used
• Amdahls Law defines the speedup that can be
  gained by using a particular feature
• The performance gain that can be obtained by
  improving some porting of a computer can be
  calculated using Amdahls Law

• The CPU Performance Equation

• Essentially all computers are constructed using a
  clock running at a constant rate
• CPU time then can be expressed by the amount of
  clock cycles

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Amdahl's Law
Speedup due to enhancement E

• Suppose that enhancement E accelerates a fraction
  F of the task by a factor S, and the remainder of
  the task is unaffected

This fraction enhanced

• Fractionenhanced the fraction of the execution
  time in the original machine that can be
  converted to take advantage of the enhancement
• Speedupenhanced the improvement gained by the
  enhanced execution mode

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Amdahl's Law
Fractionenhanced
ExTimenew ExTimeold x (1 - Fractionenhanced)
Speedupenhanced
1
ExTimeold ExTimenew
Speedupoverall

Fractionenhanced
(1 - Fractionenhanced)
Speedupenhanced
This fraction enhanced
ExTimeold
ExTimenew
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Amdahl's Law

• Example Floating point (FP) instructions are
  improved to run faster by a factor of 2 but only
  10 of actual instructions are FP. Whats the
  overall speedup gained by this improvement?

Answer

• Amdahls Law can serve as a guide to how much an
  enhancement will improve performance and how to
  distribute resource to improve cost-performance
• It is particularly useful for comparing
  performances both of the overall system and the
  CPU of two design alternatives

More example page 41 and page 42
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CPU Performance

• All computers are constructed using a clock to
  operate its circuits
• Typically measured by two basic metrics
• Clock rate today in MHz and GHz
• Clock cycle time clock cycle time 1/clock rate
• E.g., 1 GHz rate corresponds to 1 ns cycle time
• Thus the CPU time for a program is given by
• Or,

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CPU Performance

• More typically, we tend to count instructions
  executed, known as Instruction Count (IC)
• CPI average number of clock cycles per
  instruction
• Hence, alternative method to get the CPU time

Depends on IS of the computer and its compiler

• CPU performance is equally dependent upon 3
  characteristics clock cycle time, CPI, IC. They
  are independent and making one better often makes
  another worse because the basic technologies
  involved in changing each characteristics are
  interdependent.

• Clock cycle time hardware technology and
  organization
• CPI organization and instruction set
  architecture
• IC instruction set architecture and complier
  technology

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CPU Performance
Example Suppose we have 2 implementations of the
same instruction set architecture. Computer A has
a clock cycle time of 10 nsec and a CPI of 2.0
for some program, and computer B has a clock
cycle time of 20 nsec and a CPI of 1.2 for the
same program. What machine is faster for this
program?
Answer Assume the program require IN
instructions to be executed CPU clock cycleA
IN x 2.0 CPU clock cycleB IN x 1.2 CPU timeA
IN x 2.0 x 10 20 x IN nsec CPU timeB IN x
1.2 x 20 24 x IN nsec So computer A is faster
than computer B.
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CPU Performance

• Often the overall performance is easy to deal
  with on a per instruction set basis
• The overall CPI can be expressed as

times instruction i is executed
CPI for instruction i
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Cycles Per Instruction
Example Suppose we have a machine where we can
count the frequency with which instructions are
executed. We also know how many cycles it takes
for each instruction type.

• Base Machine (Reg / Reg)
• Instruction Freq CPI ( Time)
• ALU 50 1 (33)
• Load 20 2 (27)
• Store 10 2 (13)
• Branch 20 2 (27)
• (Total CPI 1.5)

How do we get total CPI? How do we get time?
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CPU Performance
Example Suppose we have made the following
measurements Frequency of FP operations (other
than FPSQR) 25 Avg. CPI of FP
operations 4.0 Avg. CPI of other
instructions 1.33 Frequency of FPSQR 2
CPI of FPSQR 20 Compare two
designs 1) decrease CPI of FPSQR to 2 2)
decrease the avg. CPI of all FP operations to 2.5.
Answer First, find the original CPI The CPI
with design 1 The CPI with design 2
So design 2 is better
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Principle of Locality

• Other important fundamental observations come
  from the properties of programs.
• Principle of locality Programs tend to reuse
  data and instructions they have used recently.
• There are two different types of locality (Ref.
  Chapter 5)
• Temporal Locality (locality in time) If an item
  is referenced, it will tend to be referenced
  again soon (loops, reuse, etc.)
• Spatial Locality (locality in space/location)
  If an item is referenced, items whose addresses
  are close one another tend to be referenced soon
  (straight line code, array access, etc.)
• We can predict with reasonable accuracy what
  instructions and data a program will use in the
  near future based on its accesses in the past.
• A program spends 90 of its execution time in
  only 10 of the code.

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Some Misleading Performance Measures

• There are certain computer performance measures
  which are famous with computer manufactures and
  sellers but may be misleading.
• MIPS (Million Instructions Per Second)
• MIPS depends on the instruction set to make it
  difficult to compare MIPS of different
  instructions.
• MIPS varies between programs on the same computer
  different programs use different instruction
  mix.
• MIPS can vary inversely to performance most
  importantly.

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Some Misleading Performance Measures

• MFLOPS Focus on one type of work
• MFLOPS (Million Floating-point Operations Per
  Second) depends on the program. Must be FP
  intensive.
• MFLOPS depends on the computer as well.
• The floating-point operations vary in complexity
  (e.g., add divide).

• Peak Performance Performance that the
  manufacture guarantees you wont exceed
• Difference between peak performance and average
  performance is huge.
• Peak performance is not useful in predicting
  observed performance.

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Summary

• 1.1 Introduction
• 1.2 The Changing Face of Computing and the Task
  of the Computer Designer
• 1.3 Technology Trends
• 1.4 Cost, Price, and Their Trends
• 1.5 Measuring and Reporting Performance
• Benchmarks
• (Weighted) Arithmetic Mean, Normalized Geometric
  Mean
• 1.6 Quantitative Principles of Computer Design
• Amdahls Law
• CPU Performance CPU Time, CPI
• Locality