Computer Organization and Design
The Hardware/Software Interface
Chapter 1
Computer Abstractions and Technology
The Computer Revolution
Progress in computer technology
■ Underpinned by Moore's Law
Makes novel applications feasible
Computers in automobiles Cell phones Human genome project World Wide Web Search Engines
Computers are pervasive
Chapter 1 — Computer Abstractions and Technology —
Classes of Computers
Desktop 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
Embedded computers
■ Hidden as components of systems
■ Stringent power/performance/cost constraints
Chapter 1
— Computer Abstractions and Technology — 3
The Processor Market
□ Cell Phones ■ PCs □ TVs
d£ <# <# cp0 c?N #        c# # #
Chapter 1 — Computer Abstractions and Technology — 4
What You Will Learn
How programs are translated into the machine language
■ And how the hardware executes them
The hardware/software interface What determines program performance
■ And how it can be improved
How hardware designers improve performance
What is parallel processing
Chapter 1 — Computer Abstractions and Technology — 5
Understanding Performance
Algorithm
■ Determines number of operations executed
Programming language, compiler, architecture
■ Determine number of machine instructions executed per operation
Processor and memory system
■ 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 — 6
Below Your Program
Hardware
Application software
■ Written in high-level language
System software
■ Compiler: translates HLL code to machine code
■ Operating System: service code
- Handling input/output
- Managing memory and storage Scheduling tasks & sharing resources
Hardware
■ Processor, memory, I/O controllers
14'
Chapter 1 — Computer Abstractions and Technology —
Levels of Program Code
High-level language
■ Level of abstraction closer to problem domain
■ Provides for productivity and portability
Assembly language
■ Textual representation of instructions
Hardware representation
■ Binary digits (bits)
■ Encoded instructions and data
4"
High-level language program (inC)
Assembly language program (for MIPS)
swap(int v[], int k) lint temp;
temp = v[k];
v[k] = v[k+l];
v[k+l] = temp;
swap:
muli $2, add $2,
1 w 1 w sw sw jr
$15. $16, $16, $15. $31
55,4
;4,$2
0($2) 4( $2) 0($2) 4($2)
Binary machine 0000000010100001000000000001 1000 language 00000000000110000001100000100001 program 10001100011000100000000000000000 (for MIPS) 10001 1001 11 100100000000000000100
10101100111100100000000000000000 10101100011000100000000000000100 00000011111000000000000000001000
Chapter 1 — Computer Abstractions and Technology — 8
Components of a Computer
The BIG Picture
Evaluating performance
Interface
Compiler
Same components for all kinds of computer
■ Desktop, server, embedded
Input/output includes
User-interface devices
Display, keyboard, mouse
■ Storage devices
Hard disk, CD/DVD, flash
Network adapters
For communicating with other computers
14
Chapter 1 — Computer Abstractions and Technology —
Anatomy of a Computer
Input device
Network cable
14"
Chapter 1 — Computer Abstractions and Technology — 10
Anatomy of a Mouse
Optical mouse
LED illuminates desktop
■ Small low-res camera
■ Basic image processor
Looks for x, y movement
■ Buttons & wheel
Supersedes roller-ball mechanical mouse
Chapter 1 — Computer Abstractions and Technology — 11
Through the Looking Glass
LCD screen: picture elements (pixels)
■ Mirrors content of frame buffer memory
Frame buffer
Y
o
Raster scan CRT display
o
X0 X1
x0 x1
14'
Chapter 1 — Computer Abstractions and Technology — 12
Opening the Box
3298504545^030
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 — 14
Inside the Processor
AMD Barcelona: 4 processor cores
Chapter 1 — Computer Abstractions and Technology — 15
Abstractions
The BIG Picture
Abstraction helps us deal with complexity
■ Hide lower-level detail
Instruction set architecture (ISA)
■ The hardware/software interface
Application binary interface
■ The ISA plus system software interface
Implementation
■ The details underlying and interface
Chapter 1 — Computer Abstractions and Technology — 16
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)
4"
Chapter 1 — Computer Abstractions and Technology — 17
Networks
Communication and resource sharing Local area network (LAN): Ethernet
■ Within a building
Wide area network (WAN: the Internet Wireless network: WiFi, Bluetooth
Chapter 1 — Computer Abstractions and Technology — 18
Technology Trends
Electronics technology continues to evolve
■ Increased capacity and performance
■ Reduced cost
i—i—i—i—i—i—i—i—i—i—i—i—i
1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
Year of introduction
DRAM capacity
Year	Technology	Relative performance/cost
1951	Vacuum tube	1
1965	Transistor	35
1975	Integrated circuit (IC)	900
1995	Very large scale IC (VLSI)	2,400,000
2005	Ultra large scale IC	6,200,000,000
14'
Chapter 1 — Computer Abstractions and Technology — 19
Defining Performance
Which airplane has the best performance?
Boeing 777
Boeing 747
BAC/Sud Concorde
Douglas DC-8-50
100    200    300    400 500
□ Passenger Capacity
Boeing 777
Boeing 747
BAC/Sud Concorde
Douglas DC-8-50
500
1000
1500
□ Cruising Speed (mph)
14'
Boeing 777
Boeing 747
BAC/Sud Concorde
Douglas DC-8-50
2000   4000  6000   8000 10000
□ Cruising Range (miles)
Boeing 777
Boeing 747
BAC/Sud Concorde
Douglas DC-8-50
100000 200000 300000 400000
□ Passengers x mph
Chapter 1 — Computer Abstractions and Technology — 20
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...
Chapter 1 — Computer Abstractions and Technology — 21
Relative Performance
Define Performance = 1 /Execution Time "X is n time faster than Y"
Performanc ex/Performanc eY
= Execution timeY/Execution timex = 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
Chapter 1 — Computer Abstractions and Technology — 22
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 — 23
CPU Clocking
Operation of digital hardware governed by a constant-rate clock
Clock (cycles)
Data transfer and computation
Update state
Clock period-
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X Ö
r
6
Clock period: duration of a clock cycle
- e.g., 250ps = 0.25ns = 250x10~12s
Clock frequency (rate): cycles per second
. e.g., 4.0GHz = 4000MHz = 4.0x109Hz
Chapter 1 — Computer Abstractions and Technology
— 24
CPU Time
CPU Time = CPU Clock Cycles x 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 — 25
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 x clock cycles How fast must Computer B clock be?
Clock CyclesA = CPU TimeA x Clock RateA
Clock Rate
Clock CyclesB   1.2 x Clock CyclesA
B
CPUTimeD 6s
10sx2GHz = 20x109
Clock Rate
B
1.2x20x109 _24x109 6s       ~ 6s
4GHz
M 14
®
Chapter 1 — Computer Abstractions and Technology — 26
Instruction Count and CPI
Clock Cycles = Instruction Count x Cycles per Instruction
CPU Time = Instruction Count x CPI x Clock Cycle Time
_ Instruction Count x 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 — 27
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 a = Instruction Count x CPIA x Cycle Time^
= Ix 2.0 x 250ps = Ix 500ps «--
A is faster.
CPU Timeg = Instruction Count x CPIg x Cycle Time^
= lx1.2x500ps = lx600ps CPUTimeB _ |x600ps CPUTimeA ~ Ix500ps
= 1.2
...by this much
Chapter 1 — Computer Abstractions and Technology — 28
CPI in More Detail
If different instruction classes take different numbers of cycles
n
Clock Cycles = ^ (CPI; x Instructio n Count j)
i=1
Weighted average CPI
Clock Cycles
n
r       Instructio n Count ^
Qpi _     v^i^urx uyoiuo     _ -y
Instructio n Count '  Instruction County
CPU
v_ _y
Relative frequency
Chapter 1 — Computer Abstractions and Technology — 29
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 = 2x1 + 1x2 + 2x3
= 10
. Avg. CPI = 10/5 = 2.0
Sequence 2: IC = 6
■ Clock Cycles = 4x1 +1x2 + 1x3
= 9
. Avg. CPI = 9/6 = 1.5
Chapter 1 — Computer Abstractions and Technology — 30
Performance Summary
The BIG Picture
Instructions Clockcycles Seconds CPU Time =-x-J--x
Program     Instruction Clockcycle
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 — 31
Power Trends
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4"
Chapter 1 — Computer Abstractions and Technology — 32
Reducing Power
Suppose a new CPU has
■ 85% of capacitive load of old CPU
■ 15% voltage and 15% frequency reduction
Pnew = C0,d x0.85 x(Voldx0.85)2 xFoldx0.85 = Q^4 = Q^ "old C0id x V0,d x F0,d
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 — 33
Uniprocessor Performance
10,000
1000
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Intel Xeon, 3.6 GHz ^64-bit Intel Xeon, 3.6 GHz AMD Opteron, 2.2 GHz^S*^* 6505 Intel Pentium 4,3.0 GHz)r<^5364 AMD Athlon, 1.6 GHz      4195 Intel Pentium III, 1.0 GHr/ 2584 Alpha 21264A, 0.7 GHz^*1779 Alpha 21264, 0.6 GHz »^1267		
Alpha 21164, 0.6 GHz^-*'993 Alpha 21164, 0.5 GHzjr^>*''649 /..''481 Alpha 21164, 0.3 GHzäO' /S 280 Alpha 21064A, 0.3 GHz«r >7-' 183 =20% PowerPC 604, 0.1GHz*>'ii7		
Alpha 21064, 0.2 GHzSs' X»'80 HP PA-RISC, 0.05 GHz ffS /.'51 IBM RSeOOO/540^ MIPS M2000^ MIPS M/120*^g		
Sun-4/260^--g		
VAX 8700*	'5	
VAX-11/780   .--*'* /		
/ ^ ^ r.^J5%^ea^5iVAX_11/785 / i**-.-                                                                           i                     i                     i                     i                     ■                     i                     i                     i /		■
1978      1980      1982      1984      1986      1988      1990      1992      1994      1996      1998      2000      2002/    2004 2006
Constrained by power, instruction-level parallelism, memory latency
4"
Chapter 1 — Computer Abstractions and Technology — 34
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 — 35
Manufacturing ICs
Silicon ingot
Packaged dies
□	□	□
□	□	□
Tested dies □ □
□ □□□□
□ □HDD □ □□□
D □
Blank wafers
Tested wafer
Tested packaged dies
Part	
tester	
□	□		
□	□	□	-
20 to 40 processing steps
Patterned wafers
	1 Wafer
	tester
Yield: proportion of working dies per wafer
Chapter 1 — Computer Abstractions and Technology — 36
AMD Opteron X2 Wafer
X2: 300mm wafer, 117 chips, 90nm technology X4: 45nm technology
14
Chapter 1 — Computer Abstractions and Technology — 37
Integrated Circuit Cost
^ . .. Cost per wafer Cost per die = —
Dies per wafer x Yield Dies per wafer « Wafer area/Die area
Yield =---=■
(1 +(Defects per area x Die area/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 — 38
SPEC CPU Benchmark
Programs used to measure performance
■ Supposedly typical of actual workload
Standard Performance Evaluation Corp (SPEC)
■ 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)
®
Chapter 1 — Computer Abstractions and Technology — 39
CINT2006 for Opteron X4 2356
Name	Description	ICx109	CPI	Tc (ns)	Exec time	Ref time	SPECratio
perl	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 (Al)	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 (Al)	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
Geometric mean							11.7
High cache miss rates
®
Chapter 1 — Computer Abstractions and Technology — 40
SPEC Power Benchmark
Power consumption of server at different workload levels
■ Performance: ssj_ops/sec
■ Power: Watts (Joules/sec)
14
Chapter 1 — Computer Abstractions and Technology — 41
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
Overall sum	1,283,590	2,605
£ssj_ops/ Xpower		493
®
Chapter 1
— Computer Abstractions and Technology
Pitfall: Amdahl's Law
Improving an aspect of a computer and expecting a proportional improvement in overall performance
t I affected t"
improved = improvement factor+ unaffected
Example: multiply accounts for 80s/100s
■ How much improvement in multiply performance to get 5* overall?
20 = —+ 20      ■ Can't be done!
n
Corollary: make the common case fast
Chapter 1 — Computer Abstractions and Technology —
Fallacy: Low Power at Idle
Look back at X4 power benchmark
- At100%load:295W
. At 50% load: 246W (83%)
- At 10% load: 180W (61%)
Google data center
■ Mostly operates at 10% - 50% load
■ At 100% load less than 1% of the time
Consider designing processors to make power proportional to load
Chapter 1 — Computer Abstractions and Technology — 44
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
MIPS-  Instruction count
Execution timex 10
Instruction count Clockrate ~ InstructioncountxCPI Hri6~CPIx106
-xl 0
Clockrate
■ CPI varies between programs on a given CPU
Chapter 1 — Computer Abstractions and Technology — 45
Concluding Remarks
Cost/performance is improving
■ Due to underlying technology development
Hierarchical layers of abstraction
■ 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
Chapter 1 — Computer Abstractions and Technology —