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Transcript of Chapter 1 Computer Abstractions and Technology 20100906
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Computer Abstractionsand Technology
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Chapter 1 Computer Abstractions and Technology 2
The Computer Revolution
Progress in computer technology
Underpinned by Moores Law
Makes novel applications feasible
Computers in automobiles
Cell phones
Human genome project
World Wide Web
Search Engines
Computers are pervasive1.1Introdu
ction
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Chapter 1 Computer Abstractions and Technology 3
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
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Chapter 1 Computer Abstractions and Technology 4
The Computer/Processor Market
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Chapter 1 Computer Abstractions and Technology 5
What You Will Learn
Computer organization
Design a faster, and cheaper computer system
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
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Chapter 1 Computer Abstractions and Technology 6
Below Your Program
Application software
Written in high-level language (HLL)
E.g. MS Word, Power Point
System software
Compiler: translates HLL code to machinecode
Operating System: service code
Handling input/output
Managing memory and storage Scheduling tasks & sharing resources
Hardware
Processor, memory, I/O controllers
1.2
BelowYourProgram
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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
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Levels of RepresentationFrom High Level Language to Hardware Language
High Level Language
Program (e.g., C)
Assembly LanguageProgram (e.g.,MIPS)
Machine LanguageProgram (MIPS)
Hardware Architecture Description(e.g., Verilog Language)
Compiler
Assembler
MachineInterpretation
temp = v[k];
v[k] = v[k+1];
v[k+1] = temp;
lw $t0, 0($2)lw $t1, 4($2)sw $t1, 0($2)sw $t0, 4($2)
0000 1001 1100 0110 1010 1111 0101 1000
1010 1111 0101 1000 0000 1001 1100 0110
1100 0110 1010 1111 0101 1000 0000 1001
0101 1000 0000 1001 1100 0110 1010 1111
Logic Circuit Description(Verilog Language)
ArchitectureImplementation
wire [31:0] dataBus;
regFile registers (databus);
ALU ALUBlock (inA, inB, databus);
wire w0;
XOR (w0, a, b);
AND (s, w0, a);
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Abstractions
* Coordination of many levels of abstraction
I/O systemProcessor
Compiler
Operating
System
(Windows)
Application (Netscape)
Digital Design
Circuit Design
Instruction SetArchitecture
Datapath & Control
transistors
MemoryHardware
Software Assembler
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Instruction Set Architecture
Also called architecture
A very important abstraction interface between hardware and low-level software
Includes instructions, registers, memory access, I/O and so on
advantage: different implementations of the same architecture
disadvantage: sometimes prevents using new innovations
True or False: Binary compatibility is extraordinarily important?
Modern instruction set architectures: IA-32, PowerPC, MIPS, SPARC, ARM, and others
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Chapter 1 Computer Abstractions and Technology 11
Components of a Computer
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
1
.3UndertheCo
vers
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Chapter 1 Computer Abstractions and Technology 12
Anatomy of a Computer
Outputdevice
Inputdevice
Inputdevice
Networkcable
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Chapter 1 Computer Abstractions and Technology 13
Opening the Box
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Chapter 1 Computer Abstractions and Technology 14
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)
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Chapter 1 Computer Abstractions and Technology 15
Networks
Communication and resource sharing
Local area network (LAN): Ethernet
Within a building
Wide area network (WAN): the Internet
Wireless network: WiFi, Bluetooth
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Chapter 1 Computer Abstractions and Technology 16
Inside the Processor (CPU)
Datapath: performs operations on data Control: sequences datapath, memory, ...
Cache memory
Small fast SRAM memory for immediateaccess to data
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Chapter 1 Computer Abstractions and Technology 17
Inside the Processor
AMD Barcelona: 4 processor cores
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Chapter 1 Computer Abstractions and Technology 18
Why do all computer look alike?
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Back to History: Milestones
Chapter 1 Computer Abstractions and Technology 19
ENIACthe worlds first
general-purpose
electronic computer
1936 1944/1946 1947Transistors
John Von NeumannStored program
1958Integrated
circuits
IBM 360Family of computersCompatible computer
1964 1971
Intel 4004First uP
1977
Apple IPersonalcomputer
Information from wiki
Alan TuringUniversal computing
machine
1961IBM StretchInstruction
pipeline
1980RISC
Load-storeArch
SUN/MIPS
1970C.mmp
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von Neumann Architecture a stored-program digital computer that uses a central processing unit (CPU)
and a single separate storage structure ("memory") to hold both instructionsand data.
von Neumann bottleneck Throughput between CPU and memory
Memory speed is much slower than CPU (Needs a cache memory, in chapter 5)
Chapter 1 Computer Abstractions and Technology 20
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Other CPUs beyond von Neumann Arch.
Modified Harvard arch (in DSP)
separated instruction and dataFound in DSP like TI OMAP in handset
Dataflow architecture
No program counterFound in graphic processing, network routing
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Chapter 1 Computer Abstractions and Technology 22
Abstractions
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
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Chapter 1 Computer Abstractions and Technology 23
Technology Trends
Electronics technology
continues to evolve
Increased capacity and
performance
Reduced cost
Year Technology Relative performance/cost
1951 Vacuum tube 1
1965 Transistor 351975 Integrated circuit (IC) 900
1995 Very large scale IC (VLSI) 2,400,000
2005 Ultra large scale IC 6,200,000,000
DRAM capacity
T h l T d M C it
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Technology Trends: Memory Capacity
(Single-Chip DRAM)size
Year
1000
10000
100000
1000000
10000000
100000000
1000000000
1970 1975 1980 1985 1990 1995 2000
year size (Mbit)
1980 0.0625
1983 0.25
1986 1
1989 4
1992 16
1996 64
1998 1282000 256
2002 512 Now 1.4X/yr, or 2X every 2 years.
8000X since 1980!
T h l T d Mi
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Year
1000
10000
100000
1000000
10000000
100000000
1970 1975 1980 1985 1990 1995 2000
i80386
i4004
i8080
Pentium
i80486
i80286
i8086
Technology Trends: Microprocessor
Complexity
2X transistors/Chip
Every 1.5 years
Called
Moores Law
Alpha 21264: 15 million
Pentium Pro: 5.5 million
PowerPC 620: 6.9 millionAlpha 21164: 9.3 millionSparc Ultra: 5.2 million
Moores Law
Athlon (K7): 22 Million
Itanium 2: 410 Million
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Technology Trends: Processor Performance
0
100
200
300
400
500
600
700
800
900
87 88 89 90 91 92 93 94 95 96 97
DEC Alpha
21264/600
DEC Alpha 5/500
DEC Alpha 5/300
DEC Alpha 4/266
IBM POWER 100
1.54X/yr
Intel P4 2000 MHz(Fall 2001)
Well talk about processor performance later on
year
Performan
cemeasure
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Computer Technology - Dramatic Change!
MemoryDRAM capacity: 2x / 2 years (since 96);64x size improvement in last decade.
Processor
Speed 2x / 1.5 years (since 85);100X performance in last decade.
Disk
Capacity: 2x / 1 year (since 97)250X size in last decade.
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Chapter 1 Computer Abstractions and Technology 28
Why do you need to know the technology trend?Or What is the impact of technology trend tocomputer design
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The meaning of technology drive
Forecastthe expected design performance
within the next few years
It change the waythat you design yourcomputer, and software Multi-processor, VLIW, parallel processor,
efficient access via caches Size, speed, probability
Other technology may change the way of thecomputer/processor design Quantum computer, DNA computer, bio-computer
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Chapter 1 Computer Abstractions and Technology 30
Performance? How fast is my computer?
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Chapter 1 Computer Abstractions and Technology 31
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
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Chapter 1 Computer Abstractions and Technology 32
Defining Performance
Which airplane has the best performance?
0 100 200 300 400 500
Douglas
DC-8-50
BAC/Sud
Concorde
Boeing 747
Boeing 777
Passenger Capacity
0 2000 4000 6000 8000 10000
Douglas DC-
8-50
BAC/Sud
Concorde
Boeing 747
Boeing 777
Cruising Range (miles)
0 500 1000 1500
Douglas
DC-8-50
BAC/Sud
Concorde
Boeing 747
Boeing 777
Cruising Speed (mph)
0 100000 200000 300000 400000
Douglas DC-
8-50
BAC/Sud
Concorde
Boeing 747
Boeing 777
Passengers x mph
1.4Performance
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Chapter 1 Computer Abstractions and Technology 33
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?
Well focus on response time for now
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Chapter 1 Computer Abstractions and Technology 34
Relative Performance
Define Performance = 1/Execution Time
X is n time faster than Y
n XY
YX
timeExecutiontimeExecution
ePerformancePerformanc
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
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Chapter 1 Computer Abstractions and Technology 35
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
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Chapter 1 Computer Abstractions and Technology 36
CPU Clocking
Operations of digital hardware governed by aconstant-rate clock
Clock (cycles)
Data transferand computation
Update state
Clock period
Clock period: duration of a clock cycle e.g., 250ps = 0.25ns = 2501012s
Clock frequency (rate): cycles per second
e.g., 4.0GHz = 4000MHz = 4.0109Hz
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Chapter 1 Computer Abstractions and Technology 37
CPU Time
Performance improved by Reducing number of clock cycles
Increasing clock rate
Hardware designer must often trade off clockrate against cycle count
RateClock
CyclesClockCPUTimeCycleClockCyclesClockCPUTimeCPU
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Chapter 1 Computer Abstractions and Technology 38
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?
4GHz6s
1024
6s
10201.2RateClock
10202GHz10s
RateClockTimeCPUCyclesClock
6s
CyclesClock1.2
TimeCPU
CyclesClockRateClock
99
B
9
AAA
A
B
BB
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Chapter 1 Computer Abstractions and Technology 39
Instruction Count and CPI
Instruction Count for a program
Determined by program, ISA and compiler
Average cycles per instruction (CPI)
Determined by CPU hardware
If different instructions have different CPI Average CPI affected by instruction mix
RateClock
CPICountnInstructio
TimeCycleClockCPICountnInstructioTimeCPUnInstructioperCyclesCountnInstructioCyclesClock
C
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Chapter 1 Computer Abstractions and Technology 40
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?
1.2500psI
600psI
ATimeCPU
BTimeCPU
600psI500ps1.2I
BTimeCycle
BCPICountnInstructio
BTimeCPU
500psI250ps2.0I
ATimeCycleACPICountnInstructioATimeCPU
A is faster
by this much
CPI i M D il
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Chapter 1 Computer Abstractions and Technology 41
CPI in More Detail
If different instruction classes take differentnumbers of cycles
n
1i
ii )CountnInstructio(CPICyclesClock
Weighted average CPI
n
1i
i
i CountnInstructio
CountnInstructio
CPICountnInstructio
CyclesClock
CPI
Relative frequency
CPI E l
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Chapter 1 Computer Abstractions and Technology 42
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 2IC in sequence 2 4 1 1
Sequence 1: IC = 5
Clock Cycles= 21 + 12 + 23= 10
Avg. CPI = 10/5 = 2.0
Sequence 2: IC = 6
Clock Cycles= 41 + 12 + 13= 9
Avg. CPI = 9/6 = 1.5
P f S
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Chapter 1 Computer Abstractions and Technology 43
Performance Summary
Performance depends on
Algorithm: affects IC, possibly CPI
Programming language: affects IC, CPI
Compiler: affects IC, CPI
Instruction set architecture: affects IC, CPI, Tc
cycleClock
Seconds
nInstructio
cyclesClock
Program
nsInstructioTimeCPU
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Chapter 1 Computer Abstractions and Technology 44
What Improvement can I gain if I speedupsomething?
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Limit of speedup Gain
1
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Chapter 1 Computer Abstractions and Technology 46
enhance: 0.5 + 0.5 = 1 enhance4=>4/(4+1) = 0.8 = F
,enhance1=> S = 4/1 = 4
4 + 1speedup = -----------------------= ----------= 2.5
1 + 1 (Amdahls Law):
1 1speedup = ------------------------ = -------------------------
((1 - 0.8) + 0.8/4) (1 0.8) + 0.8/4 When S ->, speedup -> 5
A d hl' L
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Chapter 1 Computer Abstractions and Technology 47
Speedup due to enhancement E:
Suppose that enhancement E accelerates a fraction F of the task bya factor S and the remainder of the task is unaffected then,
Ew/oePerformanc
Ew/ePerformanc
Ew/TimeExecution
Ew/oTimeExecutionSpeedup(E)
E)Time(w/oExecution)S
FF)((1E)Time(w/Execution
F1
1
S
FF)-(1
1E)Speedup(w/ S
Amdahl's Law
Pitf ll A d hl L
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Chapter 1 Computer Abstractions and Technology 48
Pitfall: Amdahls Law
Improving an aspect of a computer and
expecting a proportional improvement inoverall performance
1.8FallaciesandPitfalls
2080
20 n
Cant be done!
unaffectedaffected
improved T
factortimprovemen
TT
Example: multiply accounts for 80s/100s
How much improvement in multiply performance toget 5 overall?
Corollary: make the common case fast
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Chapter 1 Computer Abstractions and Technology 49
CPU Time is great? But how fast is my computer?Or how faster is my computer than that one?
P f M t
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Chapter 1 Computer Abstractions and Technology 50
Performance Measurement
Two different machines X and Y.
X is ntimes faster than Y
Since execution time is the reciprocal of performance
Says n-1 = m/100
This concludes that X is m% faster than Y
nX
Y
timeExecution
timeExecution
Y
X
X
Y
ePerformanc
ePerformanc
timeExecution
timeExecution n
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Chapter 1 Computer Abstractions and Technology 51
What Programs for Comparison?
Whats wrong with this program as a workload?
integer A[][], B[][], C[][];
for (I=0; I
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Chapter 1 Computer Abstractions and Technology 52
5 Levels of Programs Used for Evaluation
Real applications
Portability, compiler, OS Modified (or scripted) applications
To enhance portability or to focus on one particular aspect of systemperformance
Kernels
Small, key pieces from real programs Best way to isolate performance of individual features
Toy benchmark
10~100 code lines
Usually, the user already knows the evaluation results
Synthetic benchmark
Whetstone, Dhrystone
Be created artificially to match an average execution profile
No user runs it
SPEC CPU Benchmark
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Chapter 1 Computer Abstractions and Technology 53
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)
n
n
1i
iratiotimeExecution
CINT2006 f O t X4 2356
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Chapter 1 Computer Abstractions and Technology 54
CINT2006 for Opteron X4 2356
Name Description IC109 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 (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 6.0Geometric mean 11.7
High cache miss rates
Reporting Performance
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Chapter 1 Computer Abstractions and Technology 55
Reporting Performance
Guiding principle: reproducible
List everything another experimenter would need to duplicate the results(especially, the input set)
Example
Hardware
CPU 3.2-GHz Pentium 4 Extreme EditionL3 Cache size 2048KB (I+D) on chip
Memory 4 x 512 MB
Disk subsystem 1 x 80GB ATA/100 7200RPM
SoftwareOS Windows XP Professional SP1
Compiler Intel C++ Compiler 7.1
C /S i P f
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Chapter 1 Computer Abstractions and Technology 56
Compare/Summarize Performance
WLOG, 2 different ways
1. Arithmetic mean
Timei is the execution time for the ith program in the workload
Weighted arithmetic mean
Weighti factors add up to 1
2. Geometric mean
To normalize to a reference machine (e.g. SPEC) Execution time ratioi is the execution time normalized to the reference
machine, for the ith program
n
in 1iTime
1
n
i 1
ii TimeWeight
n
n
i
i1
ratiotimeExecution
Which one is better ? Or is there any difference?
Example
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Chapter 1 Computer Abstractions and Technology 57
Example
1. The arithmetic mean performance varies from ref. to ref.2. The geometric mean performance is consistent
Remark
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Chapter 1 Computer Abstractions and Technology 58
Remark
SPECRatio is just a ratio rather than an absolute esecution time
Note that when comparing 2 computers as a ratio, execution times on
the reference computer drop out, so choice of reference computer is
irrelevant
B
A
A
B
B
reference
A
reference
B
A
ePerformanc
ePerformanc
imeExecutionT
imeExecutionT
imeExecutionT
imeExecutionT
imeExecutionT
imeExecutionT
SPECRatio
SPECRatio
25.1e.g.
SPEC CINT2000 and CFP2000 Rating for
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Chapter 1 Computer Abstractions and Technology 59
gPentium III and 4 at Different Clock Rates
1.Performance scales with the clock frequency. Not the usual case.
Losses in memory system is not presented.
2. Pentium III performs better for CINT2000 than for CFP2000.
Pentium 4 is reverse.
Pitf ll MIPS P f M t i
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Chapter 1 Computer Abstractions and Technology 60
Pitfall: MIPS as a Performance Metric
MIPS: Millions of Instructions Per Second
Doesnt account for
Differences in ISAs between computers
Differences in complexity between instructions
66
6
10CPI
rateClock
10rateClock
CPIcountnInstructio
countnInstructio
10timeExecution
countnInstructioMIPS
CPI varies between programs on a givenCPU
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Chapter 1 Computer Abstractions and Technology 61
Why do we need parallel processing?Fact behind multiprocessor?
Uniprocessor Performance
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Chapter 1 Computer Abstractions and Technology 62
Uniprocessor Performance1
.6TheSeaCha
nge:TheSwitchtoMultiprocessors
Constrained by power, instruction-level parallelism,memory latency
Power Trends
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Chapter 1 Computer Abstractions and Technology 63
Power Trends
In CMOS IC technology
1.5ThePowerWall
FrequencyVoltageloadCapacitivePower 2
100030 5V 1V
Power is a Limiter
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64
Power is a Limiter
5KW18KW
1.5KW
500W
40048008
80808085
8086286
386486
Pentium
0.1
1
10
100
1000
10000
100000
1971 1974 1978 1985 1992 2000 2004 2008
Power(Watts)
Power delivery and dissipation will be prohibitive !Source: Borkar, De Intel
P6
transitionfrom NMOSto CMOS
Power Density will Increase
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65
Power Density will Increase
40048008
8080
8085
8086
286 386 486Pentium
P6
1
10
100
1000
10000
1970 1980 1990 2000 2010
PowerDensity(W/cm2)
Hot Plate
Nuclear
Reactor
Rocket
Nozzle
Power densities too high to keep junctions at low temps
Suns
Surface
Source: Borkar, De Intel
Reducing Power
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Reducing Power
Suppose a new CPU has
85% of capacitive load of old CPU
15% voltage and 15% frequency reduction
0.520.85FVC
0.85F0.85)(V0.85C
P
P4
old
2
oldold
old
2
oldold
old
new
The power wall
We cant reduce voltage further We cant remove more heat
How else can we improve performance?
Multiprocessors
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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 Power Benchmark
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SPEC Power Benchmark
Power consumption of server at differentworkload levels
Performance: ssj_ops/sec
Power: Watts (Joules/sec)
10
0i
i
10
0i
i powerssj_opsWattperssj_opsOverall
SPECpower ssj2008 for X4
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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 1800% 0 141
Overall sum 1,283,590 2,605
ssj_ops/ power 493
Fallacy: Low Power at Idle
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Fallacy: Low Power at Idle
Look back at X4 power benchmark
At 100% 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 makepower proportional to load
Concluding Remarks
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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
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oncludingRem