Computer Technology Performance Metrics
CS 154: Computer Architecture Lecture #3
Winter 2020
Ziad Matni, Ph.D.
Dept. of Computer Science, UCSB
Administrative
• Lab 01 – how did Friday go?
• Gradescope account?
• Piazza account?
• Remember: due date is Wednesday on Gradescope!
1/13/20 Matni, CS154, Wi20 2
Job/Help Opportunity
Disabled Students Program Notetaker Needed
CMPSC 154 MW 12:30
$25 per unit (of the class)
(prorated based on the number of weeks for which they are selected)
Questions can be sent to DSP Notetaking Email: notes@sa.ucsb.edu
Potential Notetakers can apply online at http://dsp.sa.ucsb.edu/services
1/13/20 Matni, CS154, Wi20 3
Lecture Outline
• Tech Details • Trends
• Historical context
• The manufacturing process of ICs
• Important Performance Measures • CPU time
• CPI
• Other factors (power, multiprocessors) • Pitfalls
1/13/20 Matni, CS154, Wi20 4
Single-Thread Processor Performance
[ Hennessy & Patterson, 2017 ]
5
Computing Devices for General Purposes
• Charles Babbage (UK)
• Analytical Engine could calculate polynomial
functions and differentials
• Inspired by older generation of calculating machines made by Blaise Pascal (1623-1662, France)
• Calculated results, but also
stored intermediate findings
(i.e. precursor to computer memory)
• “Father of Computer Engineering” • Ada Byron Lovelace (UK)
• Worked with Babbage and foresaw computers doing much more than calculating numbers
• Loops and Conditional Branching
• “Mother of Computer Programming” 1/13/20
C. Babbage (1791
–
1871)
A. Byron Lovelace (1815
–
1852)
Part of Babbage’s Analytical Engine
Images from Wikimedia.org
6
The Modern Digital Computer
• Calculating machines kept being produced in the early 20th century (IBM was established in the US in 1911)
• Instructions were very simple, which made hardware implementation easier, but this hindered the creation of complex programs.
Alan Turing (UK)
• Theorized the possibility of computing machines capable of performing any conceivable mathematical computation as long as this was representable as an algorithm
• Called “Turing Machines” (1936) – ideas live on today…
• Lead the effort to create a machine to successfully decipher the
German “Enigma Code” during World War II
A. Turing (1912
–
1954)
1/13/20 Matni, CS154, Wi20
7
Zuse Z3 (1941)
• Built by Konrad Zuse in wartime Germany using 2000 relays
• Could do floating-point arithmetic with hardware
• 22-bit word length ; clock frequency of about 4–5 Hz!!
• 64 words of memory!!!
• Two-stage pipeline
1) fetch & execute, 2) writeback
• No conditional branch
• Programmed via paper tape
Replica of the Zuse Z3 in the Deutsches Museum, Munich
1/13/20
[Venusianer, Creative Commons BY-SA 3.0 ]
ENIAC (1946)
• First electronic general-purpose computer
• Constructed during WWII to calculate firing tables for US Army • Trajectories (for bombs) computed in 30 seconds instead of 40 hours • Was very fast for its time – started to replace human “computers”
• Used vacuum tubes (transistors hadn’t been invented yet)
• Weighed 30 tons, occupied 1800 sq ft
• It used 160 kW of power (about 3000 light bulbs worth)
• It cost $6.3 million in today’s money to build.
• Programmed by plugboard and switches, time consuming!
• As a result of large number of tubes, it was often broken
(5 days was longest time between failures!)
1/13/20 Matni, CS154, Wi20 9
ENIAC
Changing the program could take days!
1/13/20 Matni, CS154, Wi20 10
[Public Domain, US Army Photo]
Comparing today’s cell phones (with dual CPUs), with ENIAC, we see they
cost 17,000X less
are 40,000,000X smaller use 400,000X less power are 120,000X lighter AND…
are 1,300X more powerful.
1/13/20 Matni, CS154, Wi20 11
EDVAC (1951)
• ENIAC team started discussing stored-program concept to speed up programming and simplify machine design
• Based on ideas by John von Nuemann & Herman Goldstine • Still the basis for our general CPU architecture today
1/13/20 Matni, CS154, Wi20 12
Commercial computers:
BINAC (1949) and UNIVAC (1951) at EMC
• Eckert and Mauchly left academia and formed the Eckert- Mauchly Computer Corporation (EMC)
• World’s first commercial computer was BINAC which didn’t work…
• Second commercial computer was UNIVAC
• Famously used to predict presidential election in 1952 • Eventually 46 units sold at >$1M each
1/13/20 Matni, CS154, Wi20 13
IBM 650 (1953)
• The first mass-produced computer
• Low-end system aimed at businesses rather than scientific enterprises
• Almost 2,000 produced
1/13/20 Matni, CS154, Wi20 14
[Cushing Memorial Library and Archives, Texas A&M, Creative Commons Attribution 2.0 Generic ]
Improvements in C.A.
• IBM 650’s instruction set architecture (ISA)
• 44 instructions in base instruction set, expandable to 97 instructions
• Hiding instruction set completely from programmer using the concept of high-level languages like Fortran (1956), ALGOL (1958) and COBOL (1959)
• Allowed the use of stack architecture, nested loops, recursive calls, interrupt handling, etc…
Adm. Grace Hopper (1906 – 1992), inventor of several High-level language concepts
1/13/20 Matni, CS154, Wi20 [Public Domain, wikimedia] 15
Manufacturing ICs
Yield: the proportion of working dies per wafer; often expressed as a number between 0 and 1
1/13/20 Matni, CS154, Wi20 16
Example: Intel Core i7 Wafer
• 300mm (diameter) wafer
• 280 chips
• Each chip is 20.7 mm x 10.5 mm
• 32nm CMOS technology
(the size of the smallest piece of logic
and the type of Silicon semiconductor used)
1/13/20 Matni, CS154, Wi20 17
Costs of Manufacturing ICs
• Wafer cost and area are fixed
• Defect rate determined by manufacturing process
• Die area determined by architecture and circuit design
1/13/20 Matni, CS154, Wi20 18
Examples
A 300 mm wafer of silicon has 500 die on it, of which 100 are not working or malfunctioning. What is the yield of this wafer?
• Y = Ngood/Ntotal = 400/500 = 80%
If the wafer costs $200, what is the cost per die?
• Cost per die = ($200)/(500 * 0.8) = $200/400 = $0.50
A 300 mm wafer of silicon has N dies that are 0.5 mm x 1 mm each.
What is N?
• Area of wafer/Area of each die
= (p * (300/2 * 10-3)2) / (0.5 * 1 * 10-6) = 141,370.605
So, N = 141,370 (round down)
1/13/20 Matni, CS154, Wi20 19
Response Time and Throughput
• Response time (aka Latency)
• How long it takes to do a fixed task
• Throughput
• Total work done per a fixed 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?
1/13/20 Matni, CS154, Wi20 20
Latency vs. Throughput
Which is more important?
• They are different.
• It depends on what your goals are… • Scientific program? Latency
• Web server? Throughput
• Example: Move people 10 miles
1/13/20
Matni, CS154, Wi20
21
• Via car: • Via bus:
• Latency:
• Throughput:
capacity = 5, speed = 60 mph capacity = 60, speed = 20 mph
car = 10 minutes, bus = 30 minutes
car = 15 PPH, bus = 60 PPH (consider round-trips)
Performance Measures
• Execution Time: Total response time, including EVERYTHING • CPU time (processing), I/O use, OS overhead, any idle time
• This determines system performance
• CPU time:
• Time spent just processing a given job
(discounts I/O time, OS time, etc…) • CPU time = user CPU time + system CPU time
• Define Performance = 1/Execution Time • Relative performance
• The performance of system A vs performance of system B, ie. PA / PB 1/13/20 Matni, CS154, Wi20 22
CPU Clocking
• Most digital hardware today operates to a constant-rate clock • Clock period: duration of a clock cycle
• e.g.250ps=0.25ns=250×10–12s
• Clock frequency: clock rate or cycles per second
• e.g.4.0GHz=4000MHz=4.0x109Hz • Hertz (Hz) is “cycles per second”, so
clock freq. = 1 / clock period
1/13/20 Matni, CS154, Wi20 23
Useful Prefixes (Multipliers) to Know
Prefix
Symbol
Multiplier
In words…
Scientific Notation
Kilo
k
1,000
thousand
103
Mega
M
1,000,000
million
106
Giga
G
1,000,000,000
billion
109
Tera
T
1,000,000,000,000
trillion
1012
Peta
P
1,000,000,000,000,000
quadrillion
1015
Prefix
Symbol
Multiplier
In words…
Scientific Notation
milli
m
0.001
thousandth
10-3
micro
μ
0.000001
millionth
10-6
nano
n
0.000000001
billionth
10-9
pico
p
0.000000000001
trillionth
10-12
1/13/20 Matni, CS154, Wi20 24
CPU Time
• Performance can be improved (i.e. make CPU Time less) by
• Reducing number of clock cycles
• Increasing clock rate
• Hardware designer must often trade off clock rate against cycle count
• Example: it took the CPU 1000 cycles to run the program. The clock cycle time (i.e. period) is 10 ns, so the CPU time is: 1000x10ns=10000ns=10μs,or10x10-6 s
1/13/20 Matni, CS154, Wi20 25
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, then Average CPI is affected by instruction mix
• Example: next slide
1/13/20 Matni, CS154, Wi20 26
CPI Example
• Computer A: Cycle Time = 250 ps, CPI = 2.0 • Computer B: Cycle Time = 500 ps, CPI = 1.2 • Same Instruction Set Architecture (ISA)
• Which is faster?
• CPU Time = Instruction Count x CPI x Cycle Time
• CPU_Time_A=NIx2.0x250x10-12 s=NIx500x10-12 s • CPU_Time_B=NIx1.2x500x10-12 s=NIx600x10-12 s • So, CPU A is faster than CPU B
• By how much is it faster?
• RelativePerformance=NIx600x10-12 s/NIx500x10-12 s=1.2
• So, CPU A is 1.2 times faster than B (or you could say it’s 20% faster)
1/13/20 Matni, CS154, Wi20 27
CPI Example using Weighted Classes
• An instruction class = instruction type
• e.g. arithmetic type vs. branching type vs. jump type, etc…
• A CPU compiles code sequences using instructions in classes A, B, C
• Sequence1:IC=5,soClockCycles=2×1+1×2+2×3=10 • So, Avg. CPI = 10/5 = 2.0
• Sequence2:IC=6,soClockCycles=4×1+1×2+1×3=9 • So,Avg.CPI=9/6=1.5
1/13/20 Matni, CS154, Wi20 28
Other Factors to CPU Performance:
Power Consumption
Market trends DEMAND that power consumption of CPUs keep decreasing.
Power and Performance DON’T always go together…
• Power = Capacitive Load x Voltage2 x Clock Frequency • So:
• Decreasing Voltage helps to get lower power, but it can make individual logic go slower!
• Increasing clock frequency helps performance, but increases power! • It’s a dilemma that has contributed to Moore’s Law “plateau”
1/13/20 Matni, CS154, Wi20 29
YOUR TO-DOs for the Week
• BRING YOUR MIPS REF CARDS TO CLASS!!! • Do your reading for next class (see syllabus)
• Finish up Assignment #1 for lab (lab01)
• You have to submit it as a PDF using Gradescope • Due on Wednesday, 1/15, by 11:59:59 PM
1/13/20 Matni, CS154, Wi20 30
1/13/20 Matni, CS154, Wi20 31