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Performance
Purchasing perspective given a collection of machines, which has the
best performance ? least cost ? best performance / cost ?
Design perspective faced with design options, which has the
best performance improvement ? least cost ? best performance / cost ?
Both require basis for comparison metric for evaluation
Our goal is to understand cost & performance implications of architectural choices
Two notions of “performance”
° Time to do the task (Execution Time)
– execution time, response time, latency
° Tasks per day, hour, week, sec, ns. ..
– throughput, bandwidth
Response time and throughput often are in opposition
DC to Paris
6.5 hours
3 hours
Plane
Boeing 747
BAD/Sud Concorde
Speed
610 mph
1350 mph
Passengers
470
132
Throughput
286,700
178,200
Which has higher performance?
Example
• Time of Concorde vs. Boeing 747?
• Concord is 1350 mph / 610 mph = 2.2 times faster
= 6.5 hours / 3 hours
• Throughput of Concorde vs. Boeing 747 ?
• Concord is 178,200 pmph / 286,700 pmph = 0.62 “times faster”
• Boeing is 286,700 pmph / 178,200 pmph = 1.6 “times faster”
• Boeing is 1.6 times (“60%”)faster in terms of throughput
• Concord is 2.2 times (“120%”) faster in terms of flying time
We will focus primarily on execution time for a single job
Definitions
Performance is in units of things-per-second bigger is better
If we are primarily concerned with response time performance(x) = 1
execution_time(x)
" X is n times faster than Y" means
Performance(X)
n = ----------------------
Performance(Y)
Performance measurement
How about collection of programs? Example:
Three machines: A, B and C. Two Programs: P1 and P2.
A B C W(1) W(2) W(3)
P1 1 10 20 .5 .9 .99
P2 1000 100 20 .5 .1 .01
W(1) 500.5 55 20
Arithmetic mean: Weight i * Time i
W(2) 91.9 18 20
W(3) 2 10 20
Performance measurement
Other option: Geometric Means (Self study pages 37-39 text book)
Metrics of performance
Compiler
Programming Language
Application
DatapathControl
Transistors Wires Pins
ISA
Function Units
(millions) of Instructions per second – MIPS(millions) of (F.P.) operations per second – MFLOP/s
Cycles per second (clock rate)
Megabytes per second
Answers per monthOperations per second
Relating Processor Metrics
CPU execution time = CPU clock cycles X clock cycle time
or CPU execution time = CPU clock cycles ÷ clock rate
CPU clock cycles= Instructions X avg. clock cycles per instr.
or CPI = CPU clock cycles÷ Instructions
CPI tells us something about the Instruction Set Architecture, the Implementation of that architecture, and the program measured
Aspects of CPU PerformanceCPU time = Seconds = Instructions x Cycles x
Seconds
Program Program Instruction Cycle
CPU time = Seconds = Instructions x Cycles x Seconds
Program Program Instruction Cycle
instr. count CPI clock rate
Program
Compiler
Instr. Set Arch.
Organization
Technology
Aspects of CPU PerformanceCPU time = Seconds = Instructions x Cycles x
Seconds
Program Program Instruction Cycle
CPU time = Seconds = Instructions x Cycles x Seconds
Program Program Instruction Cycle
instr count CPI clock rate
Program X
Compiler X (x)
Instr. Set. X X
Organization X X
Technology X
Organizational Trade-offs
Compiler
Programming Language
Application
DatapathControl
Transistors Wires Pins
ISA
Function Units
Instruction Mix
Cycle Time
CPI
CPI
CPU time = ClockCycleTime * SUM CPI * Ii = 1
n
ii
CPI = SUM CPI * F where F = I i = 1
n
i i i i
Instruction Count
"instruction frequency"
Invest Resources where time is Spent!
CPI = (CPU Time * Clock Rate) / Instruction Count = Clock Cycles / Instruction Count
“Average cycles per instruction”
Example (RISC processor)
Typical Mix
Base Machine (Reg / Reg)
Op Freq Cycles CPI(i) % Time
ALU 50% 1 .5 23%
Load 20% 5 1.0 45%
Store 10% 3 .3 14%
Branch 20% 2 .4 18%
2.2
How much faster would the machine be if a better data cachereduced the average load time to 2 cycles?
How does this compare with using branch prediction to shave a cycle off the branch time?
What if two ALU instructions could be executed at once?
Example (RISC processor)
Typical Mix
Base Machine (Reg / Reg)
Op Freq Cycles CPI(i) % Time
ALU 50% 1 .5 23%
Load 20% 5 1.0 45%
Store 10% 3 .3 14%
Branch 20% 2 .4 18%
2.2
How much faster would the machine be if:A) Loads took “0” cycles? B) Stores took “0” cycles?C) ALU ops took “0” cycles?D)Branches took “0” cycles?
MAKE THE COMMON CASE FAST
Amdahl's Law
Speedup due to enhancement E:
ExTime w/o E Performance w/ E
Speedup(E) = -------------------- = ---------------------
ExTime w/ E Performance w/o E
Suppose that enhancement E accelerates a fraction F of the task
by a factor S and the remainder of the task is unaffected then,
ExTime(with E) = ((1-F) + F/S) X ExTime(without E)
Speedup(with E) = ExTime(without E) ÷ ((1-F) + F/S) X ExTime(without E)
Speedup(with E) =1/ ((1-F) + F/S)
Amdahl's Law-example
A new CPU makes Web serving 10 times faster. The old CPU spent 40% of the time on computation and 60% on waiting for I/O. What is the overall enhancement?
Fraction enhanced= 0.4
Speedup enhanced = 10
Speedup overall = 1 = 1.56
0.6 +0.4/10
Example from Quiz 1-2004
a)A program consists of 80% initialization code and of 20% code being the main iteration loop, which is run 1000 times. The total runtime of the program is 100 seconds. Calculate the fraction of the total run time needed for the initialization and the iteration. Which part would you optimize? B) The program should have a total run time of 60 seconds. How can this be achieved? (15 points)
Marketing Metrics
MIPS = Instruction Count / Time * 10^6
= Clock Rate / CPI * 10^6
•machines with different instruction sets ?
•programs with different instruction mixes ?
• dynamic frequency of instructions
• uncorrelated with performance
GFLOPS = FP Operations / Time * 10^9 playstation: 6.4 GFLOPS
•machine dependent
•often not where time is spent
Why Do Benchmarks? How we evaluate differences
Different systems Changes to a single system
Provide a target Benchmarks should represent large class of important
programs Improving benchmark performance should help many
programs For better or worse, benchmarks shape a field Good ones accelerate progress
good target for development Bad benchmarks hurt progress
help real programs v. sell machines/papers? Inventions that help real programs don’t help
benchmark
Basis of Evaluation
Actual Target Workload
Full Application Benchmarks
Small “Kernel” Benchmarks
Microbenchmarks
Cons
• representative• very specific• non-portable• difficult to run, or measure• hard to identify cause
• portable• widely used• improvements useful in reality
• easy to run, early in design cycle
• identify peak capability and potential bottlenecks
•less representative
• easy to “fool”
• “peak” may be a long way from application performance
Successful Benchmark: SPEC
1987 RISC industry mired in “bench marketing”:(“That is 8 MIPS machine, but they claim 10 MIPS!”)
EE Times + 5 companies band together to perform Systems Performance Evaluation Committee (SPEC) in 1988: Sun, MIPS, HP, Apollo, DEC
Create standard list of programs, inputs, reporting: some real programs, includes OS calls, some I/O
SPEC first round
First round 1989; 10 programs, single number to summarize performance
One program: 99% of time in single line of code New front-end compiler could improve dramatically
Benchmark
SPE
C P
erf
0
100
200
300
400
500
600
700
800
gcc
epre
sso
spic
e
doduc
nasa7
li
eqnto
tt
matr
ix300
fpppp
tom
catv
SPEC95
Eighteen application benchmarks (with inputs) reflecting a technical computing workload
Eight integer go, m88ksim, gcc, compress, li, ijpeg, perl, vortex
Ten floating-point intensive tomcatv, swim, su2cor, hydro2d, mgrid, applu,
turb3d, apsi, fppp, wave5 Must run with standard compiler flags
eliminate special undocumented incantations that may not even generate working code for real programs
Summary
Time is the measure of computer performance! Good products created when have:
Good benchmarks Good ways to summarize performance
If not good benchmarks and summary, then choice between improving product for real programs vs. improving product to get more sales=> sales almost always wins
Remember Amdahl’s Law: Speedup is limited by unimproved part of program
CPU time = Seconds = Instructions x Cycles x Seconds
Program Program Instruction Cycle
CPU time = Seconds = Instructions x Cycles x Seconds
Program Program Instruction Cycle
Readings & More…
Reminder:
READ:
TEXTBOOK: Chapter 1 pages 1 to 47 Moore paper (posted on course web site).
