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COMPUTER ORGANIZATION AND DESIGN
The Hardware/Software Interface
Chapter 1
Computer Abstractions
and Technology
5th
Edition
Why Computer Architecture?

Yes, I know, it’s a required class…
Chapter 1 — Computer Abstractions and Technology — 2
Why Computer Architecture?



Embarrassing if you have a CS degree
and can’t make sense of the following
terms: DRAM, pipelining, cache
hierarchies, I/O, virtual memory, …
Embarrassing if you have a CS degree
and can’t decide which processor to buy: 3
GHz P4 or 2.5 GHz Athlon
(helps us reason about
performance/power), …
Chapter 1 — Computer Abstractions and Technology — 3
Why Computer Architecture?


Obvious first step for chip designers,
compiler/OS writers
Will knowledge of the hardware help you
write better programs?
Chapter 1 — Computer Abstractions and Technology — 4
Must a Programmer Care About Hardware?




Must know how to reason about program
performance and energy
Memory management: if we understand
how/where data is placed, we can help
ensure that relevant data is nearby
Thread management: if we understand
how threads interact, we can write smarter
multi-threaded programs
Why do we care about multi-threaded
programs?
Chapter 1 — Computer Abstractions and Technology — 5
Example

Tower of Hanoi program
void main(){
int ndisks;
int frompeg = 1;
int topeg = 3;
int temppeg = 2;
cout << "Enter the number of disks you want to move (use 15)? ";
cin >> ndisks;
move(ndisks, frompeg, topeg, temppeg);
cout << "Done";
system("pause");
}
void move(int ndisks, int frompeg, int topeg, int temppeg){
if(ndisks > 1){
move(ndisks-1, frompeg, temppeg, topeg);
cout << "move disk " << ndisks << " from peg " << frompeg << " to peg " << topeg << endl;
move(ndisks-1, temppeg, topeg, frompeg);
} else {
cout << "move disk " << ndisks << " from peg " << frompeg << " to peg " << topeg << endl << endl;
}
}
Chapter 1 — Computer Abstractions and Technology — 6
Example

How to achieve a 200x speedup for matrix
vector multiplication?




Data level parallelism: 3.8x
Loop unrolling and out-of-order execution:
2.3x
Cache blocking: 2.5x
Thread level parallelism: 14x
Chapter 1 — Computer Abstractions and Technology — 7

Progress in computer technology


Makes novel applications feasible






Underpinned by Moore’s Law
§1.1 Introduction
The Computer Revolution
Computers in automobiles
Cell phones
Human genome project
World Wide Web
Search Engines
Computers are pervasive
Chapter 1 — Computer Abstractions and Technology — 8
Classes of Computers

Personal computers



General purpose, variety of software
Subject to cost/performance tradeoff
Server computers



Network based
High capacity, performance, reliability
Range from small servers to building sized
Chapter 1 — Computer Abstractions and Technology — 9
Classes of Computers

Supercomputers



High-end scientific and engineering
calculations
Highest capability but represent a small
fraction of the overall computer market
Embedded computers


Hidden as components of systems
Stringent power/performance/cost constraints
Chapter 1 — Computer Abstractions and Technology — 10
The PostPC Era
Chapter 1 — Computer Abstractions and Technology — 11
The PostPC Era

Personal Mobile Device (PMD)





Battery operated
Connects to the Internet
Hundreds of dollars
Smart phones, tablets, electronic glasses
Cloud computing




Warehouse Scale Computers (WSC)
Software as a Service (SaaS)
Portion of software run on a PMD and a
portion run in the Cloud
Amazon and Google
Chapter 1 — Computer Abstractions and Technology — 12
What You Will Learn

How programs are translated into the
machine language



The hardware/software interface
What determines program performance



And how the hardware executes them
And how it can be improved
How hardware designers improve
performance
What is parallel processing
Chapter 1 — Computer Abstractions and Technology — 13
Understanding Performance

The performance of a program depends on
many factors...both hardware and software
Chapter 1 — Computer Abstractions and Technology — 14
Understanding Performance

Algorithm


Programming language, compiler, architecture


Determine number of machine instructions executed
per operation
Processor and memory system


Determines number of operations executed
Determine how fast instructions are executed
I/O system (including OS)

Determines how fast I/O operations are executed
Chapter 1 — Computer Abstractions and Technology — 15

Design for Moore’s Law

Use abstraction to simplify design

Make the common case fast

Performance via parallelism

Performance via pipelining

Performance via prediction

Hierarchy of memories

Dependability via redundancy
§1.2 Eight Great Ideas in Computer Architecture
Eight Great Ideas
Chapter 1 — Computer Abstractions and Technology — 16

Application software


Written in high-level language
System software


Compiler: translates HLL code to
machine code
Operating System: service code




§1.3 Below Your Program
Below Your Program
Handling input/output
Managing memory and storage
Scheduling tasks & sharing resources
Hardware

Processor, memory, I/O controllers
Chapter 1 — Computer Abstractions and Technology — 17
Levels of Program Code

High-level language



Assembly language


Level of abstraction closer
to problem domain
Provides for productivity
and portability
Textual representation of
instructions
Hardware representation


Binary digits (bits)
Encoded instructions and
data
Chapter 1 — Computer Abstractions and Technology — 18
The BIG Picture

Same components for
all kinds of computer


Desktop, server,
embedded
§1.4 Under the Covers
Components of a Computer
Input/output includes

User-interface devices


Storage devices


Display, keyboard, mouse
Hard disk, CD/DVD, flash
Network adapters

For communicating with
other computers
Chapter 1 — Computer Abstractions and Technology — 19
Touchscreen



PostPC device
Supersedes keyboard
and mouse
Resistive and
Capacitive types


Most tablets, smart
phones use capacitive
Capacitive allows
multiple touches
simultaneously
Chapter 1 — Computer Abstractions and Technology — 20
Through the Looking Glass

LCD screen: picture elements (pixels)

Mirrors content of frame buffer memory
Chapter 1 — Computer Abstractions and Technology — 21
Opening the Box
Capacitive multitouch LCD screen
3.8 V, 25 Watt-hour battery
Computer board
Chapter 1 — Computer Abstractions and Technology — 22
Inside the Processor (CPU)



Datapath: performs operations on data
Control: sequences datapath, memory, ...
Cache memory

Small fast SRAM memory for immediate
access to data
Chapter 1 — Computer Abstractions and Technology — 23
Inside the Processor

Apple A5
Chapter 1 — Computer Abstractions and Technology — 24
Abstractions
The BIG Picture

Abstraction helps us deal with complexity


Instruction set architecture (ISA)


The hardware/software interface
Application binary interface


Hide lower-level detail
The ISA plus system software interface
Implementation

Hardware that obeys the architecture
abstraction.
Chapter 1 — Computer Abstractions and Technology — 25
A Safe Place for Data

Volatile main memory


Loses instructions and data when power off
Non-volatile secondary memory



Magnetic disk
Flash memory
Optical disk (CDROM, DVD)
Chapter 1 — Computer Abstractions and Technology — 26
Networks




Communication, resource sharing,
nonlocal access
Local area network (LAN): Ethernet
Wide area network (WAN): the Internet
Wireless network: WiFi, Bluetooth
Chapter 1 — Computer Abstractions and Technology — 27

Electronics
technology
continues to evolve


Increased capacity
and performance
Reduced cost
DRAM capacity
Year
Technology
Relative performance/cost
1951
Vacuum tube
1965
Transistor
1975
Integrated circuit (IC)
1995
Very large scale IC (VLSI)
2013
Ultra large scale IC
1
35
900
2,400,000
§1.5 Technologies for Building Processors and Memory
Technology Trends
250,000,000,000
Chapter 1 — Computer Abstractions and Technology — 28
Semiconductor Technology


Silicon: semiconductor
Add materials to transform properties:



Conductors
Insulators
Switch
Chapter 1 — Computer Abstractions and Technology — 29
Manufacturing ICs

Yield: proportion of working dies per wafer
Chapter 1 — Computer Abstractions and Technology — 30
Intel Core i7 Wafer


300mm wafer, 280 chips, 32nm technology
Each chip is 20.7 x 10.5 mm
Chapter 1 — Computer Abstractions and Technology — 31
Integrated Circuit Cost
Cost per wafer
Cost per die 
Dies per wafer  Yield
Dies per wafer  Wafer area Die area
1
Yield 
(1  (Defects per area  Die area/2)) 2

Nonlinear relation to area and defect rate



Wafer cost and area are fixed
Defect rate determined by manufacturing process
Die area determined by architecture and circuit design
Chapter 1 — Computer Abstractions and Technology — 32





Which airplane has the best performance?
Boeing 777
Boeing 747
BAC/SUD Concorde
Douglas DC-8-50
§1.6 Performance
Defining Performance
Chapter 1 — Computer Abstractions and Technology — 33

Which airplane has the best performance?
Boeing 777
Boeing 777
Boeing 747
Boeing 747
BAC/Sud
Concorde
BAC/Sud
Concorde
Douglas
DC-8-50
Douglas DC8-50
0
100
200
300
400
0
500
Boeing 777
Boeing 777
Boeing 747
Boeing 747
BAC/Sud
Concorde
BAC/Sud
Concorde
Douglas
DC-8-50
Douglas DC8-50
500
1000
Cruising Speed (mph)
4000
6000
8000 10000
Cruising Range (miles)
Passenger Capacity
0
2000
§1.6 Performance
Defining Performance
1500
0
100000 200000 300000 400000
Passengers x mph
Chapter 1 — Computer Abstractions and Technology — 34
Response Time and Throughput

Response time


How long it takes to do a task
Throughput

Total work done per unit time


e.g., tasks/transactions/… per hour
How are response time and throughput affected
by


Replacing the processor with a faster version?
Adding more processors?
Chapter 1 — Computer Abstractions and Technology — 35
Response Time and Throughput

How are response time and throughput affected
by

Replacing the processor with a faster version?


Decreasing response time almost always improves
throughput. Hence this will improve both response time and
throughput.
Adding more processors?

No one task gets work done faster, so only throughput
increases.
Chapter 1 — Computer Abstractions and Technology — 36
Response Time and Throughput




Single user interested in response time
Datacenter interested in throughput or bandwidth
For now we will be primarily concerned with
response time
To maximize performance, we want to minimize
response time or execution time for some task
Chapter 1 — Computer Abstractions and Technology — 37
Relative Performance


Define Performance = 1/Execution Time
“X is n time faster than Y”
Performanc e X Performanc e Y
 Execution time Y Execution time X  n

Example: time taken to run a program



10s on X, 15s on Y
Execution TimeY / Execution TimeX
= 15s / 10s = 1.5
So X is 1.5 times faster than Y
Chapter 1 — Computer Abstractions and Technology — 38
Measuring Execution Time

Elapsed time

Total response time, including all aspects



Processing, I/O, OS overhead, idle time
Determines system performance
CPU time

Time spent processing a given job



Discounts I/O time, other jobs’ shares
Comprises user CPU time and system CPU
time
Different programs are affected differently by
CPU and system performance
Chapter 1 — Computer Abstractions and Technology — 39
CPU Clocking

Operation of digital hardware governed by a
constant-rate clock
Clock period
Clock (cycles)
Data transfer
and computation
Update state

Clock period: duration of a clock cycle


e.g., 250ps = 0.25ns = 250×10–12s
Clock frequency (rate): cycles per second

e.g., 4.0GHz = 4000MHz = 4.0×109Hz
Chapter 1 — Computer Abstractions and Technology — 40
CPU Time
CPU Time  CPU Clock Cycles  Clock Cycle Time
CPU Clock Cycles

Clock Rate

Performance improved by



Reducing number of clock cycles
Increasing clock rate
Hardware designer must often trade off clock
rate against cycle count
Chapter 1 — Computer Abstractions and Technology — 41
CPU Time Example


Computer A: 2GHz clock, 10s CPU time
Designing Computer B



Aim for 6s CPU time
Can do faster clock, but causes 1.2 × clock cycles
How fast must Computer B clock be?
Clock Cycles B 1.2  Clock Cycles A
Clock Rate B 

CPU Time B
6s
Clock Cycles A  CPU Time A  Clock Rate A
 10s  2GHz  20  109
1.2  20  109 24  109
Clock Rate B 

 4GHz
6s
6s
Chapter 1 — Computer Abstractions and Technology — 42
Instruction Count and CPI
Clock Cycles  Instructio n Count  Cycles per Instructio n
CPU Time  Instructio n Count  CPI  Clock Cycle Time
Instructio n Count  CPI

Clock Rate

Instruction Count for a program


Determined by program, ISA and compiler
Average cycles per instruction


Determined by CPU hardware
If different instructions have different CPI

Average CPI affected by instruction mix
Chapter 1 — Computer Abstractions and Technology — 43
CPI Example




Computer A: Cycle Time = 250ps, CPI = 2.0
Computer B: Cycle Time = 500ps, CPI = 1.2
Same ISA and running the same program
Which is faster, and by how much?
CPU Time
CPU Time
A
 Instructio n Count  CPI  Cycle Time
A
A
 I  2.0  250ps  I  500ps
A is faster…
B
 Instructio n Count  CPI  Cycle Time
B
B
 I  1.2  500ps  I  600ps
B  I  600ps  1.2
CPU Time
I  500ps
A
CPU Time
…by this much
Chapter 1 — Computer Abstractions and Technology — 44
CPI in More Detail

If different instruction classes take different
numbers of cycles
n
Clock Cycles   (CPIi  Instructio n Count i )
i1

Weighted average CPI
n
Clock Cycles
Instructio n Count i 

CPI 
   CPIi 

Instructio n Count i1 
Instructio n Count 
Relative frequency
Chapter 1 — Computer Abstractions and Technology — 45
CPI Example


Alternative compiled code sequences using
instructions in classes A, B, C
Class
A
B
C
CPI for class
1
2
3
IC in sequence 1
2
1
2
IC in sequence 2
4
1
1
Sequence 1: IC = 5


Clock Cycles
= 2×1 + 1×2 + 2×3
= 10
Avg. CPI = 10/5 = 2.0

Sequence 2: IC = 6


Clock Cycles
= 4×1 + 1×2 + 1×3
=9
Avg. CPI = 9/6 = 1.5
Chapter 1 — Computer Abstractions and Technology — 46
Performance Summary
The BIG Picture
Instructio ns Clock cycles
Seconds
CPU Time 


Program
Instructio n Clock cycle

Performance depends on




Algorithm: affects IC, possibly CPI
Programming language: affects IC, CPI
Compiler: affects IC, CPI
Instruction set architecture: affects IC, CPI, Tc
Chapter 1 — Computer Abstractions and Technology — 47


Figure shows the increase in clock rate and power of
eight generations of Intel microprocessors.
Both increased rapidly for decades, and then flattened off
recently.
§1.7 The Power Wall
Power Trends
Chapter 1 — Computer Abstractions and Technology — 48



Reason for their recent slowing is that we
have run into the practical power limit for
cooling the microprocessors.
In the PostPC Era the really critical
resource is energy.
Battery life trumps all other metrics
§1.7 The Power Wall
Power Trends
Chapter 1 — Computer Abstractions and Technology — 49



The dominant technology for ICs is CMOS
For CMOS, the primary source of energy
consumption is dynamic energy, i.e.
energy that is consumed when transistors
switch states from 0 to 1 and vice versa.
In CMOS IC technology
§1.7 The Power Wall
Power Trends
Power  Capacitive load  Voltage 2  Frequency
×30
5V → 1V
×1000
Chapter 1 — Computer Abstractions and Technology — 50




The capacitive load per transistor is a
function of both the number of transistors
connected to an output (fanout) and the
capacitance of both wires and transistors.
Frequency switched is a function of the
clock rate.
Problem with lowering voltage further.
Increase cooling and turning off parts.
§1.7 The Power Wall
Power Trends
Chapter 1 — Computer Abstractions and Technology — 51
Reducing Power

Suppose a new CPU has


85% of capacitive load of old CPU
15% voltage and 15% frequency reduction
Pnew Cold  0.85  (Vold  0.85) 2  Fold  0.85
4


0.85
 0.52
2
Pold
Cold  Vold  Fold

The power wall



We can’t reduce voltage further
We can’t remove more heat
How else can we improve performance?
Chapter 1 — Computer Abstractions and Technology — 52
§1.8 The Sea Change: The Switch to Multiprocessors
Uniprocessor Performance
Constrained by power, instruction-level parallelism,
memory latency
Chapter 1 — Computer Abstractions and Technology — 53
Multiprocessors

Multicore microprocessors


More than one processor per chip
Requires explicitly parallel programming

Compare with instruction level parallelism



Hardware executes multiple instructions at once
Hidden from the programmer
Hard to do



Programming for performance
Load balancing
Optimizing communication and synchronization
Chapter 1 — Computer Abstractions and Technology — 54
SPEC CPU Benchmark

Programs used to measure performance


Standard Performance Evaluation Corp (SPEC)


Supposedly typical of actual workload
Develops benchmarks for CPU, I/O, Web, …
SPEC CPU2006

Elapsed time to execute a selection of programs





Negligible I/O, so focuses on CPU performance
Consists of a set of 12 integer benchmarks
(CINT2006) and 17 floating-point benchmarks
(CFP2006)
Normalize relative to reference machine
SPECratio = Execution Time of Reference
Machine/Execution Time of Measure Machine
Bigger number indicates faster performance.
Chapter 1 — Computer Abstractions and Technology — 55
CINT2006 for Intel Core i7 920
Chapter 1 — Computer Abstractions and Technology — 56
SPEC CPU Benchmark


To simplify the marketing of computers, SPEC
decided to report a single number to summarize all 12
integer benchmarks.
Summarize as geometric mean of performance ratios

CINT2006 (integer) and CFP2006 (floating-point)
n
n
Execution time ratio
i
i1
Chapter 1 — Computer Abstractions and Technology — 57
SPEC Power Benchmark

Given the increasing importance of energy and power,
SPEC added a benchmark to measure power
Chapter 1 — Computer Abstractions and Technology — 58
SPECpower_ssj2008 for Xeon X5650
Chapter 1 — Computer Abstractions and Technology — 59
SPEC Power Benchmark

Power consumption of server at different
workload levels


Performance: ssj_ops/sec
Power: Watts (Joules/sec)
 10
  10

Overall ssj_ops per Watt    ssj_ops i    poweri 
 i 0
  i 0

Chapter 1 — Computer Abstractions and Technology — 60
Fallacy: Low Power at Idle

Look back at i7 power benchmark




Google data center



At 100% load: 258W
At 50% load: 170W (66% of peak power)
At 10% load: 121W (47% of peak power)
Mostly operates at 10% – 50% load
At 100% load less than 1% of the time
Consider designing processors to make
power proportional to load, i.e. 10% load
should use 10% of peak power
Chapter 1 — Computer Abstractions and Technology — 61


Pitfall: Expecting the improving of one aspect
of a computer to increase overall performance
by an amount proportional to the size of the
improvement.
The idea of making the common case fast
reminds us that the opportunity for
improvement is affected by how much time the
event consumes.
§1.10 Fallacies and Pitfalls
Pitfall: Amdahl’s Law
Chapter 1 — Computer Abstractions and Technology — 62
Pitfall: Amdahl’s Law



A simple design problem illustrates this well…
Suppose a program runs in 100 seconds on a
computer, with multiply operations responsible
for 80 seconds of this time. How much do I
have to improve the speed of multiplication if I
want my program to run five times faster?
The execution time of a program after making
improvement is given by a simple equation
known as Amdahl’s Law
Chapter 1 — Computer Abstractions and Technology — 63
Pitfall: Amdahl’s Law

Amdahl’s Law:
Timprov ed
Taf f ected

 Tunaf f ected
improvemen t factor
Timproved = Execution time after improvement
Taffected = Execution time affected by improvement
Tunaffected = Execution time unaffected by improvement
Improvement factor = Amount of improvement
Chapter 1 — Computer Abstractions and Technology — 64
Pitfall: Amdahl’s Law
Timprov ed
Taf f ected

 Tunaf f ected
improvemen t factor
80 seconds
Execution time after improvemen t 
 (100  80 seconds )
n
Since we want the performance to be five times faster, the new
execution time should be 20 seconds, giving
80 seconds
 (100  80 seconds )
n
80 seconds
20 seconds 
 (20 seconds )
n
80 seconds
 Can’t be done!
0
n
20 seconds 
Chapter 1 — Computer Abstractions and Technology — 65
Pitfall: Amdahl’s Law



In other words, there is no amount by which we
can enhance multiply to achieve a 5x increase
in performance, if multiply accounts for only
80% of the workload.
The performance enhancement possible with a
given improvement is limited by the amount
that the improved feature is used.
Corollary: make the common case fast
Chapter 1 — Computer Abstractions and Technology — 66
Pitfall: MIPS as a Performance Metric

MIPS: Millions of Instructions Per Second
Instructio n count
MIPS 
Execution time  10 6
Instructio n count
Clock rate


6
Instructio n count  CPI
CPI

10
6
 10
Clock rate

CPI varies between programs on a given CPU
Chapter 1 — Computer Abstractions and Technology — 67
Pitfall: MIPS as a Performance Metric

MIPS: Millions of Instructions Per Second



Easy to understand
Faster computers mean bigger MIPS
Doesn’t account for





Capabilities of the instructions
Differences in ISAs between computers
Instruction count will be different
Differences in complexity between instructions
CPI varies between programs on a given CPU
Chapter 1 — Computer Abstractions and Technology — 68

Cost/performance is improving


Hierarchical layers of abstraction



In both hardware and software
Instruction set architecture


Due to underlying technology development
§1.11 Concluding Remarks
Concluding Remarks
The hardware/software interface
Execution time: the best performance
measure
Power is a limiting factor

Use parallelism to improve performance
Chapter 1 — Computer Abstractions and Technology — 69