CS 465
Computer Architecture
Fall 2009
Lecture 01: Introduction
Daniel Barbará ( cs.gmu.edu/~dbarbara)
[Adapted from Computer Organization and Design,
Patterson & Hennessy, © 2005, UCB]
Course Administration

Instructor:
Daniel Barbará
dbarbara@gmu.edu
4420 Eng. Bldg.

Text:
Required: Computer Organization & Design –
The Hardware Software Interface, Patterson &
Hennessy, the 4th Edition
Grading Information

Grade determinates

Midterm Exam

Final Exam
Homeworks

~25%
1
~35%
~40%
- Due at the beginning of class (or, if its code to be submitted
electronically, by 17:00 on the due date). No late assignments
will be accepted.

Course prerequisites

grade of C or better in CS 367
Acknowledgements




Slides adopted from Dr. Zhong
Contributions from Dr. Setia
Slides also adopt materials from many other universities
IMPORTANT:
- Slides are not intended as replacement for the text
- You spent the money on the book, please read it!
Course Topics (Tentative)

Instruction set architecture (Chapter 2)

MIPS

Arithmetic operations & data (Chapter 3)

System performance (Chapter 4)

Processor (Chapter 5)

Datapath and control

Pipelining to improve performance (Chapter 6)

Memory hierarchy (Chapter 7)

I/O (Chapter 8)
Focus of the Course

How computers work



MIPS instruction set architecture
The implementation of MIPS instruction set architecture – MIPS
processor design
Issues affecting modern processors


Pipelining – processor performance improvement
Cache – memory system, I/O systems
Why Learn Computer Architecture?

You want to call yourself a “computer scientist”

Computer architecture impacts every other aspect of computer science

You need to make a purchasing decision or offer “expert” advice

You want to build software people use – sell many, many copies(need performance)

Both hardware and software affect performance
- Algorithm determines number of source-level statements
- Language/compiler/architecture determine machine instructions (Chapter 2
and 3)
- Processor/memory determine how fast instructions are executed (Chapter 5,
6, and 7)
- Assessing and understanding performance(Chapter 4)
Outline Today

Course logistics

Computer architectures overview

Trends in computer architectures
Computer Systems

Software



Application software – Word Processors, Email, Internet
Browsers, Games
Systems software – Compilers, Operating Systems
Hardware



CPU
Memory
I/O devices (mouse, keyboard, display, disks, networks,……..)
Software
Software
Applications
software
Systems
software
laTE X
Compilers
Operating
systems
gcc
Assemblers
as
Virtual
memory
File
system
I/O device
drivers
Instruction Set Architecture
software
instruction set
hardware

One of the most important abstractions is ISA
A
critical interface between HW and SW
 Example: MIPS
 Desired properties


Convenience (from software side)
Efficiency (from hardware side)
D.Barbará
What is Computer Architecture







Programmer’s view: a pleasant environment
Operating system’s view: a set of resources (hw
& sw)
System architecture view: a set of components
Compiler’s view: an instruction set architecture
with OS help
Microprocessor architecture view: a set of
functional units
VLSI designer’s view: a set of transistors
implementing logic
Mechanical engineer’s view: a heater!
D.Barbará
What is Computer Architecture
Patterson & Hennessy: Computer
architecture = Instruction set architecture
+ Machine organization + Hardware
 For this course, computer architecture
mainly refers to ISA (Instruction Set
Architecture)

 Programmer-visible,
serves as the boundary
between the software and hardware
 Modern ISA examples: MIPS, SPARC,
PowerPC, DEC Alpha
D.Barbará
Organization and Hardware

Organization: high-level aspects of a computer’s
design
Principal components: memory, CPU, I/O, …
 How components are interconnected
 How information flows between components
 E.g. AMD Opteron 64 and Intel Pentium 4: same ISA
but different organizations


Hardware: detailed logic design and the
packaging technology of a computer

E.g. Pentium 4 and Mobile Pentium 4: nearly identical
organizations but different hardware details
D.Barbará
Types of computers and their applications

Desktop




Run third-party software
Office to home applications
30 years old
Servers



Modern version of what used to be called mainframes,
minicomputers and supercomputers
Large workloads
Built using the same technology in desktops but higher capacity
- Expandable
- Scalable
- Reliable

Large spectrum: from low-end (file storage, small businesses) to
supercomputers (high end scientific and engineering
applications)
- Gigabytes to Terabytes to Petabytes of storage

Examples: file servers, web servers, database servers
Types of computers…

Embedded







Microprocessors everywhere! (washing machines, cell phones,
automobiles, video games)
Run one or a few applications
Specialized hardware integrated with the application (not your
common processor)
Usually stringent limitations (battery power)
High tolerance for failure (don’t want your airplane avionics to
fail!)
Becoming ubiquitous
Engineered using processor cores
- The core allows the engineer to integrate other functions into the
processor for fabrication on the same chip
- Using hardware description languages: Verilog, VHDL
Where is the Market?
Millions of Computers
1200
1122
1000
892
Embedded
Desktop
Servers
862
800
600
488
400 290
200
0
93
3
1998
114
3
1999
135
4
2000
129
4
2001
131
5
2002
In this class you will learn

How programs written in a high-level language (e.g.,
Java) translate into the language of the hardware and
how the hardware executes them.

The interface between software and hardware and how
software instructs hardware to perform the needed
functions.

The factors that determine the performance of a program

The techniques that hardware designers employ to
improve performance.
As a consequence, you will understand what features may
make one computer design better than another for a
particular application
High-level to Machine Language
Compiler
High-level language program
(in C)
Assembly language program
(for MIPS)
Assembler
Binary machine language program
(for MIPS)
Evolution…

In the beginning there were only bits… and people spent
countless hours trying to program in machine language
01100011001 011001110100

Finally before everybody went insane, the assembler
was invented: write in mnemonics called assembly
language and let the assembler translate (a one to one
translation)
Add A,B

This wasn’t for everybody, obviously… (imagine how
modern applications would have been possible in
assembly), so high-level language were born (and with
them compilers to translate to assembly, a many-to-one
translation)
C= A*(SQRT(B)+3.0)
THE BIG IDEA

Levels of abstraction: each layer provides its own
(simplified) view and hides the details of the next.
Instruction Set Architecture (ISA)

ISA: An abstract interface between the hardware and the
lowest level software of a machine that encompasses all
the information necessary to write a machine language
program that will run correctly, including instructions,
registers, memory access, I/O, and so on.
“... the attributes of a [computing] system as seen by the
programmer, i.e., the conceptual structure and functional
behavior, as distinct from the organization of the data flows and
controls, the logic design, and the physical implementation.”
– Amdahl, Blaauw, and Brooks, 1964
 Enables implementations of varying cost and performance to run
identical software

ABI (application binary interface): The user portion of the
instruction set plus the operating system interfaces used
by application programmers. Defines a standard for
binary portability across computers.
ISA Type Sales
Other
SPARC
Hitachi SH
PowerPC
Motorola 68K
MIPS
IA-32
ARM
1400
Millions of Processor
1200
1000
800
600
400
200
0
1998
1999
2000
2001
2002
PowerPoint “comic” bar chart with approximate values (see
text for correct values)
Organization of a computer
Anatomy of Computer
5 classic components
Personal Computer
Computer
Processor
Control
(“brain”)
Datapath
(“brawn”)
Memory
(where
programs,
data
live when
running)
Devices
Input
Output
Keyboard,
Mouse
Disk
(where
programs,
data
live when
not running)
Display,
Printer
Datapath: performs arithmetic operation
 Control: guides the operation of other components based on the user
instructions

PC Motherboard Closeup
Inside the Pentium 4
Moore’s Law

In 1965, Gordon Moore predicted that the number of
transistors that can be integrated on a die would double
every 18 to 24 months (i.e., grow exponentially with
time).

Amazingly visionary – million transistor/chip barrier was
crossed in the 1980’s.





2300 transistors, 1 MHz clock (Intel 4004) - 1971
16 Million transistors (Ultra Sparc III)
42 Million transistors, 2 GHz clock (Intel Xeon) – 2001
55 Million transistors, 3 GHz, 130nm technology, 250mm2 die
(Intel Pentium 4) - 2004
140 Million transistor (HP PA-8500)
Processor Performance Increase
Performance (SPEC Int)
10000
Intel Pentium 4/3000
DEC Alpha 21264A/667
DEC Alpha 21264/600
Intel Xeon/2000
1000
DEC Alpha 4/266
100
DEC AXP/500
DEC Alpha 5/500
DEC Alpha 5/300
IBM POWER 100
HP 9000/750
10
IBM RS6000
SUN-4/260
MIPS M2000
MIPS M/120
1
1987
1989
1991
1993
1995
Year
1997
1999
2001
2003
Trend: Microprocessor Capacity
100000000
Itanium II: 241 million
Pentium 4: 55 million
Alpha 21264: 15 million
Pentium Pro: 5.5 million
PowerPC 620: 6.9 million
Alpha 21164: 9.3 million
Sparc Ultra: 5.2 million
10000000
Moore’s Law
Pent ium
i80486
Transistors
1000000
i80386
i80286
100000
CMOS improvements:
• Die size: 2X every 3 yrs
• Line width: halve / 7 yrs
i8086
10000
i8080
i4004
1000
1970
1975
1980
1985
Year
1990
1995
2000
Moore’s Law

“Cramming More Components onto Integrated Circuits”


Gordon Moore, Electronics, 1965
# of transistors per cost-effective integrated circuit doubles every 18 months
“Transistor capacity doubles every 18-24 months”
Speed 2x / 1.5 years (since ‘85);
100X performance in last decade
Trend: Microprocessor Performance
Memory

Dynamic Random Access Memory (DRAM)
 The choice for main memory
 Volatile (contents go away when power is lost)
 Fast
 Relatively small

DRAM capacity: 2x / 2 years (since ‘96);
64x size improvement in last decade

Static Random Access Memory (SRAM)
 The choice for cache
 Much faster than DRAM, but less dense and more costly

Magnetic disks
 The choice for secondary memory
 Non-volatile
 Slower
 Relatively large


Capacity: 2x / 1 year (since ‘97)
250X size in last decade
Solid state (Flash) memory
 The choice for embedded computers
 Non-volatile
Memory

Optical disks



Removable, therefore very large
Slower than disks
Magnetic tape



Even slower
Sequential (non-random) access
The choice for archival
DRAM Capacity Growth
512M
256M
128M
1000000
64M
Kbit capacity
100000
16M
10000
4M
1M
1000
256K
64K
100
16K
10
1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002
Year of introduction
Trend: Memory Capacity
s ize
Growth of capacity per chip
1000000000
100000000
Bits
10000000
1000000
100000
10000
1000
1970
1975
1980
1985
1990
1995
Year
• Now 1.4X/yr, or 2X every 2 years.
• more than 10000X since 1980!
2000
year size (Mbit)
1980
0.0625
1983
0.25
1986
1
1989
4
1992
16
1996
64
1998 128
2000 256
2002 512
2006 2048
Dramatic Technology Change

State-of-the-art PC when you graduate:
(at least…)




Processor clock speed:
5000 MegaHertz
(5.0 GigaHertz)
Memory capacity:
4000 MegaBytes
(4.0 GigaBytes)
Disk capacity:
2000 GigaBytes
(2.0 TeraBytes)
New units! Mega => Giga, Giga => Tera
(Kilo, Mega, Giga, Tera, Peta, Exa, Zetta, Yotta = 1024)
Come up with a clever mnemonic, fame!
Example Machine Organization

Workstation design target



25% of cost on processor
25% of cost on memory (minimum memory size)
Rest on I/O devices, power supplies, box
Computer
CPU
Memory
Devices
Control
Input
Datapath
Output
MIPS R3000 Instruction Set Architecture

Registers
Instruction Categories




Load/Store
Computational
Jump and Branch
Floating Point
-


R0 - R31
coprocessor
PC
HI
Memory Management
Special
LO
3 Instruction Formats: all 32 bits wide
OP
rs
rt
OP
rs
rt
OP
rd
sa
immediate
jump target
funct
§1.4 Performance
Defining Performance

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
1500
0
100000 200000 300000 400000
Passengers x mph
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?
We’ll focus on response time for now…
Relative Performance

Define Performance = 1/Execution Time

“X is n time faster than Y”
Performance X Performance Y
 Execution time Y Execution time X  n

Example: time taken to run a program



10s on A, 15s on B
Execution TimeB / Execution TimeA
= 15s / 10s = 1.5
So A is 1.5 times faster than B
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
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
CPU Time

Performance improved by



Reducing number of clock cycles
Increasing clock rate
Hardware designer must often trade off clock rate against cycle
count
CPU Time  CPU Clock Cycles Clock Cycle Time
CPU Clock Cycles

Clock Rate
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 CyclesB 1.2  Clock CyclesA
Clock Rate B 

CPU Time B
6s
Clock CyclesA  CPU Time A  Clock Rate A
 10s  2GHz  20  109
1.2  20  109 24  109
Clock Rate B 

 4GHz
6s
6s
Instruction Count and CPI

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
Clock Cycles  Instruction Count  Cycles per Instruction
CPU Time  Instruction Count  CPI  Clock Cycle Time
Instruction Count  CPI

Clock Rate
CPI Example

Computer A: Cycle Time = 250ps, CPI = 2.0

Computer B: Cycle Time = 500ps, CPI = 1.2

Same ISA

Which is faster, and by how much?
CPU Time  Instruction Count  CPI  Cycle Time
A
A
A
 I  2.0  250ps  I  500ps
A is faster…
CPU Time  Instruction Count  CPI  Cycle Time
B
B
B
 I  1.2  500ps  I  600ps
CPU Time
B  I  600ps  1.2
CPU Time
I  500ps
A
…by this much
CPI in More Detail

If different instruction classes take different numbers of
cycles
n
Clock Cycles   (CPI i  Instruction Count i )
i1

Weighted average CPI
n
Clock Cycles
Instruction Count i 

CPI 
   CPI i 

Instruction Count i1 
Instruction Count 
Relative frequency
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

Sequence 2: IC = 6

Clock Cycles
= 2×1 + 1×2 + 2×3
= 10

Clock Cycles
= 4×1 + 1×2 + 1×3
=9

Avg. CPI = 10/5 = 2.0

Avg. CPI = 9/6 = 1.5
Performance Summary
The BIG Picture

Performance depends on




Algorithm: affects IC, possibly CPI
Programming language: affects IC, CPI
Compiler: affects IC, CPI
Instruction set architecture: affects IC, CPI, Tc
Instructions Clock cycles Seconds
CPU Time 


Program
Instruction Clock cycle
§1.5 The Power Wall
Power Trends

In CMOS IC technology
Pow er  Capacitive load  Voltage2  Frequency
×30
5V → 1V
×1000
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?
Constrained by power, instruction-level parallelism,
memory latency
§1.6 The Sea Change: The Switch to Multiprocessors
Uniprocessor Performance
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
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


Normalize relative to reference machine
Summarize as geometric mean of performance ratios
- CINT2006 (integer) and CFP2006 (floating-point)
n
n
Execution time ratio
i
i1
CINT2006 for Opteron X4 2356
IC×109
CPI
Tc (ns)
Exec time
Ref time
SPECratio
Interpreted string processing
2,118
0.75
0.40
637
9,777
15.3
bzip2
Block-sorting compression
2,389
0.85
0.40
817
9,650
11.8
gcc
GNU C Compiler
1,050
1.72
0.47
24
8,050
11.1
mcf
Combinatorial optimization
336
10.00
0.40
1,345
9,120
6.8
go
Go game (AI)
1,658
1.09
0.40
721
10,490
14.6
hmmer
Search gene sequence
2,783
0.80
0.40
890
9,330
10.5
sjeng
Chess game (AI)
2,176
0.96
0.48
37
12,100
14.5
libquantum
Quantum computer simulation
1,623
1.61
0.40
1,047
20,720
19.8
h264avc
Video compression
3,102
0.80
0.40
993
22,130
22.3
omnetpp
Discrete event simulation
587
2.94
0.40
690
6,250
9.1
astar
Games/path finding
1,082
1.79
0.40
773
7,020
9.1
xalancbmk
XML parsing
1,058
2.70
0.40
1,143
6,900
6.0
Name
Description
perl
Geometric mean
11.7
High cache miss rates
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_opsi    pow eri 
 i 0
  i 0

SPECpower_ssj2008 for X4
Target Load %
Performance (ssj_ops/sec)
Average Power (Watts)
100%
231,867
295
90%
211,282
286
80%
185,803
275
70%
163,427
265
60%
140,160
256
50%
118,324
246
40%
920,35
233
30%
70,500
222
20%
47,126
206
10%
23,066
180
0%
0
141
1,283,590
2,605
Overall sum
∑ssj_ops/ ∑power
493

Improving an aspect of a computer and expecting a proportional
improvement in overall performance
Timproved 

Taffected
 Tunaffected
improvemen t factor
Example: multiply accounts for 80s/100s

How much improvement in multiply performance to get 5× overall?
80
20 
 20
n


Corollary: make the common case fast
Can’t be done!
§1.8 Fallacies and Pitfalls
Pitfall: Amdahl’s Law
Fallacy: Low Power at Idle

Look back at X4 power benchmark




Google data center



At 100% load: 295W
At 50% load: 246W (83%)
At 10% load: 180W (61%)
Mostly operates at 10% – 50% load
At 100% load less than 1% of the time
Consider designing processors to make power
proportional to load
Pitfall: MIPS as a Performance Metric

MIPS: Millions of Instructions Per Second

Doesn’t account for
- Differences in ISAs between computers
- Differences in complexity between instructions
Instruction count
MIPS 
Execution time  10 6
Instruction count
Clock rate


6
Instruction count  CPI
CPI

10
6
 10
Clock rate

CPI varies between programs on a given CPU

Cost/performance is improving


Hierarchical layers of abstraction


Due to underlying technology development
In both hardware and software
Instruction set architecture

The hardware/software interface

Execution time: the best performance measure

Power is a limiting factor

Use parallelism to improve performance
§1.9 Concluding Remarks
Concluding Remarks